"Bridging the Gap: How Clinical Decision Support Systems Are Transforming Healthcare—One Alert at a Time"

The Role of CDSS in Clinical Decision-Making

Clinical Decision Support Systems (CDSS) have emerged as effective tools in recent healthcare, providing evidence-based advice to clinicians at the point of care. In the case of Mr. John Smith, the CDSS played a pivotal role in guiding Dr. Carter through a complex diagnostic process, by generating differential diagnoses, flagging medication interactions, and recommending timely tests like the D-dimer and EKG, the technology enabled Dr. Carter to identify and treat a life-threatening pulmonary embolism quickly.

Research supports the effectiveness of CDSS in healthcare. A study by Bright et al. (2012) found that CDSS significantly improved adherence to clinical guidelines, reduced medication errors, and enhanced preventive care measures.

Challenges and Solutions

The implementation of CDSS at Metro Health Hospital revealed several challenges that are common in healthcare settings. These include:

Alert Fatigue: Excessive notifications can overwhelm clinicians, leading to ignored or missed alerts.
Solution: Implement intelligent filtering mechanisms to prioritize high-priority alerts and reduce unnecessary notifications. For example, using machine learning algorithms to tailor alerts based on patient-specific risk factors and clinician preferences can minimize disruptions (Ash et al., 2007).
Workflow Disruption: Clinicians reported that the CDSS disrupted their workflow, particularly when alerts were intrusive or poorly timed.
Solution: Design CDSS interfaces that integrate seamlessly into existing workflows. User-centered design principles, such as customizable dashboards and context-sensitive alerts, can enhance usability and reduce resistance (Bates et al., 2003).
Data Discrepancies: Inconsistencies between Health Information Exchange (HIE) records and internal EHRs caused delays in care.
Solution: Standardize data formats and improve interoperability between systems. Regular audits and real-time data synchronization can ensure accuracy and reliability (Blumenthal & Tavenner, 2010).
Patient-Centered Approach

A patient-centered approach is essential for maximizing the benefits of CDSS. By incorporating patient input, CDSS can deliver more personalized and effective care. For instance, integrating patient-reported outcomes (PROs) into the system can provide valuable insights into symptoms, preferences, and treatment goals. In Mr. Smith’s case, the CDSS could have included prompts to discuss his concerns about anticoagulation therapy, ensuring that his preferences were considered in the treatment plan.

Conclusion

The case of Dr. Carter and Mr. Smith highlights the transformative potential of CDSS in healthcare, providing timely, evidence-based recommendations. Healthcare institutions can ensure CDSS supports, not replaces, clinical judgment by refining these systems to line with clinical workflows and introducing patient-centered features.

References

Ash, J. S., Sittig, D. F., Campbell, E. M., Guappone, K. P., & Dykstra, R. H. (2007). Some unintended consequences of clinical decision support systems. AMIA Annual Symposium Proceedings, 2007, 26–30. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655822/Links to an external site.Links to an external site.

Bates, D. W., Kuperman, G. J., Wang, S., Gandhi, T., Kittler, A., Volk, L., Spurr, C., Khorasani, R., Tanasijevic, M., & Middleton, B. (2003). Ten commandments for effective clinical decision support: Making the practice of evidence-based medicine a reality. Journal of the American Medical Informatics Association, 10(6), 523–530. https://doi.org/10.1197/jamia.M1370Links to an external site.Links to an external site.

Blumenthal, D., & Tavenner, M. (2010). The "meaningful use" regulation for electronic health records. New England Journal of Medicine, 363(6), 501–504. https://doi.org/10.1056/NEJMp1006114Links to an external site.Links to an external site.

Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., Samsa, G., Hasselblad, V., Williams, J. W., & Musty, M. D. (2012). Effect of clinical decision-support systems: A systematic review. Annals of Internal Medicine, 157(1), 29–43. https://doi.org/10.7326/0003-4819-157-1-201207030-00450Links to an external site.

"The Doctor’s New Best Friend: CDSS"

Clinical Decision Support Systems (CDSS) are hoped to transform healthcare through evidence-based guidance on diagnosis, treatment, and prevention that, in turn, should strengthen the whole decision-making process and enhance the quality of care delivery. Dr. Emily Carter of Metro Health Hospital witnessed how much CDSS can assist a physician in the clinic, while the integration brought numerous issues. This blog investigates CDSS in clinical decision-making, the main challenges, and a patient-centered model designed to enhance its use.

1. The Role of CDSS in Clinical Decision-Making

CDSS provides essentially timely, evidence-based help for the clinician. In Dr. With excellent functionality, CDSS advised doing tests like the D-dimer and an EKG for a 63-year-old male with a past medical history of hypertension and diabetes, befitting the diagnosis of pulmonary embolism. The latter is a good example of how CDSS can drive prompt decisions concerning critical diagnostics and immediate interventions that helped Dr. Emily Carter.
With contradictions in the information between Health Information Exchange (HIE) records and internal EHRs can act as hiccups, as shown in this case study. Moreover, alert fatigue can contributing to diminished long-term effectiveness of the function among clinicians.

2. Challenges and Solutions
CDSS has a host of challenges on its road to acceptance, from the resistance of clinicians to technology to disruption in the workflow, and so on. The health systems within Dr. Carter's hospital faced alert fatigue, and there were issues regarding the functioning of the system. Recommendations are to optimize alert thresholds and make notifications more meaningful and less annoying. Attention to improving user interfaces, and enhancing ongoing training, should help clinicians maximize usefulness. In addition, more integration between EHR and CDSS would enhance the flow of information across departments and lessen delays.

3. Patient-Centered Approach
It is pertinent that there is inclusion of patient preferences and patient values in support structures for Clinical Decisions. In the context of Mr. Smith, the system recommended tests based on his medical history, but integrating patient-reported outcomes (PROs) would further enhance personalized care. For example, if Mr. Smith preferred a certain diagnosis to be made through noninvasive methods, most perhaps it would suggest alternatives.
Denecke et al. (2019) have shown, through research, that taking patient preference into account enhances the overall experiences of satisfaction with CDSS and subsequently improves adherence to treatment. Participating in making an informed decision is also attributed to improvement in health (Muench et al., 2020). When CDSS assumes a patient-centered approach, there develops an interactive environment for clinical encounters.
In conclusion, CDSS changes the way clinical decision-making is approached, thereby providing clinical evidence to support clinicians, improving their accuracy in diagnostic evaluation, and minimizing errors. Alert fatigue, integration, and the hesitance of clinicians, however, need to be addressed through careful planning and training. With the patient in focus, CDSS empowers individualization of care, which in turn promises increased patient satisfaction and enhancement of health over time. CDSS will subsequently take a deeper role in future health care. This list will expand as it becomes integrated into global healthcare.

References

1. Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011). The benefits of health information technology: A review of the recent literature shows predominantly positive results. *Health Affairs, 30*(3), 464-471. https://doi.org/10.1377/hlthaff.2011.0178

2. Denecke, K., Deng, Y., & Gorbunova, I. (2019). Patient-centered decision support systems: A review of methods, applications, and challenges. *International Journal of Medical Informatics, 124*, 61-72. https://doi.org/10.1016/j.ijmedinf.2019.01.003

3. Topaz, M., McCullagh, M., & Ware, P. (2020). Leveraging clinical decision support systems for early detection: The role of predictive analytics in preventing adverse health events. *Journal of Healthcare Management, 65*(4), 252-264. https://doi.org/10.1097/JHM-D-19-00291

"CDSS in Healthcare: Improving Healthcare Outcomes and Unlocking the Challenges"

Clinical Decision Support Systems (CDSS) plays a critical role in the decision-making process by collecting accurate data and collectively work with IT and clinicians to improve patient outcomes. CDSS focuses on data collection and segmentation, develops algorithms, identifies trends, and creates frameworks for physicians to be more efficient and effective in the decision-making process. Let’s look at the emergency department, where time is of the essence, and CDSS is vital in capturing lab results and medical interactions and prompting patient alerts to help clinicians take immediate action such as the case study with Mr. John Smith. However, many argue that CDSS requires the human element and interaction. It draws on explicit, implicit, latent, and tacit knowledge since “considerable knowledge exists in the minds of highly trained and experienced professionals, some of which is accessed through interactions between professionals and by nature cannot be incorporated into the CDSS. Information systems are primarily decision support and not decision making” (Mastrian & McGonigle, 2021). If these systems are not effectively designed to incorporate patient inputs and allows for clinicians to have full autonomy, clinicians will continue to contribute to the approximately 250,000 deaths that happen every year due to medical errors.

The challenges that are associated with CDSS are staff receptiveness to new systems due to a very complex workflow or design, designing a system without the user in mind, and ineffective User Acceptance Testing (UAT) These issues align with the three axioms that are discussed in the textbook "Informatics for Health Professionals" in Chapter 8. If these axioms fail to be considered in the process, it could lead to a significant financial loss, such as Cedars-Sinai Medical Center in Los Angeles, which had to shut down a $34 million system after three months of launching due to medical staff rebellion. Moreover, it was evident in the case study where Dr. Carter acknowledges clinicians’ frustration to the workflow, confusion and system discrepancies. Developing an effective CDSS goes beyond the design and implementation phase, it becomes an enterprise issue, “the challenge for healthcare leaders is not only developing IT to support a knowledge-based system but also transforming the current structure of their healthcare organization” (Brown, Pasupathy, & Patrick, 2019).
The solution lies with innovative leaders who fully understand the importance of CDSS. These leaders must collaborate and align not only with medical staff and clinicians but with the entire organization to ensure effective communication throughout the development phase and, in turn, achieve a successful implementation by also integrating the ten commandments.

The patient-centered approach aims to enhance and promote patient engagement, including the caregiver and viewed as a team. It creates trust between patients and trust is established when patients feel heard, set attainable health goals, and respond to patient concerns in a timely manner, keeping the patient top of mind. It is necessary to expand the five rights not only to the clinician but also to patients where, “the right information should incorporate patient-centered information like patient-generated health data. The right person may extend to caregivers as well as patients and caregivers, depending on the patient. The right format and the right channel should consider patient preferences. For example, patients may prefer receiving information via an app on their smartphone versus logging into their patient portal. Finally, the right time in the workflow should be inclusive of accounting for the patient’s daily activities (ie, the patient lifeflow)” (Dullabh et al., 2024).

References

Brown, G. D., Pasupathy, K. S., & Patrick, T. B. (2019). Health informatics : a systems perspective (Second edition.). Health Administration Press.

Dullabh, P., Dhopeshwarkar, R., Cope, E., Gauthreaux, N., Zott, C., Peterson, C., Leaphart, D., Hoyt, S., Hammer, A., Ryan, S., Swiger, J., Lomotan, E. A., & Desai, P. (2024). Advancing patient-centered clinical decision support in today’s health care ecosystem: key themes from the Clinical Decision Support Innovation Collaborative’s 2023 Annual Meeting. JAMIA Open, 7(4), ooae109-. https://doi.org/10.1093/jamiaopen/ooae109

Kathleen Mastrian, & Dee McGonigle. (2021). Informatics for Health Professionals: Vol. Second edition. Jones & Bartlett Learning.

"Revolutionizing Healthcare: How CDSS Enhances Clinical Decision-Making"

Healthcare organizations maintain steady dependence on technology for achieving optimal patient results within their progressively changing systems. Healthcare providers benefit from Clinical Decision Support Systems (CDSS) connected to Electronic Health Records (EHR) since the evidence-based information enables medical staff to develop precise diagnoses and administer treatment solutions (Sutton et al., 2020). The CDSS adoption at Metro Health Hospital showed Dr. Emily Carter all the positive aspects and technical difficulties that CDSS systems present. CDSS systems enhance medical quality by improving diagnostic accuracy and treatment effectiveness, although health providers need to create successful alert fatigue reduction strategies alongside maintaining workflow consistency for patient-centered care operations.

1. The Role of CDSS in Clinical Decision-Making

The diagnostic process becomes easier through CDSS since the system performs patient data analysis to produce prompt advice. Dr. Carter used the clinical decision support system (CDSS) to perform the assessment of patient Mr. John Smith, who presented with chest pain symptoms with accompanying shortness of breath while being a 63-year-old male. The system provided the provider access to possible alternate conditions and medication interaction alerts and D-dimer and EKG testing instructions (Shahmoradi et al., 2021). Patient stabilization occurred because the physician diagnosed pulmonary embolism by immediately starting anticoagulation treatment. Healthcare provider misdiagnoses decline alongside standard patient care delivery through CDSS implementation, according to Moghadam et al. (2021). CDSS supports medical staff in following clinical protocols according to Sutton et al. (2020) by minimizing errors that improve patient security. CDSS benefits have multiple constraints that limit their implementation.

2. Challenges and Solutions

Medical staff working at Metro Health managed care received too many alert notifications, which ultimately reduced their ability to detect critical safety alerts despite desensitizing them to nonessential notifications. The delivery of crucial system alerts through a multi-step process should be implemented by hospitals before their systems eliminate redundant and minor alarm notifications (Sutton et al., 2020). Another challenge was workflow disruption. Particular medical practitioners stated that CDSS systems disrupted their professional autonomy because they forced physicians to carry out extra diagnostic steps within their treatment methods. These CDSS interfaces should allow healthcare professionals to change recommendation settings that support their clinical experience while maintaining data use for their decision-making process (Sutton et al., 2020). Confusion developed among healthcare providers because medical data located in hospital electronic health records differed from those present in external health information exchanges. Healthcare service interruptions will decrease as standards emerge that allow different medical systems to integrate properly.

3. A Patient-Centered Approach to CDSS

The main objective of CDSS functions is to help healthcare providers, while it ought to deliver identical functionalities to patients. Medical care develops customization through healthcare provider implementation of patient-submitted data from wearable technology and symptom tracking (Moghadam et al., 2021). The patient interface should have been designed for patients to be able to report symptoms so hospital staff could provide intervention earlier.
The core activity of shared decision-making functions as an essential healthcare methodology to help medical delivery. The clear presentation of care options provided by CDSS together with benefit-risk assessments guides patients' decision-making process (Shahmoradi et al., 2021). CDSS systems link with patient preference models, allowing medical staff to help diabetes and hypertension patients develop integrated therapeutic and lifestyle treatment plans.

4. Conclusion

The adoption of CDSS technology enhances clinical decision quality as it offers instant evidence-based suggestions to medical practitioners, according to Dr. Carter's case study. Each hospital must solve information fatigue problems among medical personnel before implementing CDSS systems properly between departments. The health outcomes for patients and their clinicians will improve when CDSS systems put patient needs first. The future of health care transformation relies on enhanced CDSS systems that present patients with simple, operational pathways to healthcare resources.

Harnessing the Power of Clinical Decision Support Systems: Balancing Innovation and Patient-Centered Care
Clinical Decision Support Systems (CDSS) have become effective implements that increase patient outcomes and decision-making in a time when technology is converting healthcare. But putting them into practice is not without its challenges. Dr. Emily Carter's experience at Metro Health Hospital offers an intriguing case study of the benefits and limitations of CDSS in modern healthcare.
1. The Role of CDSS in Clinical Decision-Making
By combining real-time patient data with massive volumes of medical data, CDSS improves clinical decision-making. Dr. Carter's identification of Mr. John Smith, a 63-year-old patient with diabetes and hypertension, was greatly aided by CDSS. The system warned her of possible drug interactions and offered differential diagnoses based on his symptoms, medical history, and current prescriptions. As a result, a pulmonary embolism was promptly discovered, enabling prompt and efficient treatment.
Although CDSS greatly increases diagnostic efficiency and accuracy, it has drawbacks as well. Metro Health Hospital doctors were worried that too many alerts, or alert fatigue, could cause people to ignore important cautions. Several of them also believed that the system infringed upon their clinical autonomy, which would have lessened their dependence on knowledge and experience.
2. Challenges and Solutions
1. Alert Fatigue: One of the primary obstacles to the implementation of CDSS is the devastating number of alerts, which may desensitize doctors and lead them to ignore important warnings. A solution to this is the customization of alert thresholds, ensuring that only high-priority notifications require immediate attention (Ancker et al., 2017).
2. Workflow Disruptions: At first, integrating CDSS into regular clinical procedures may cause workflow disruptions. At Metro Health, doctors had to adapt to new procedures, often at the sacrifice of productivity. To address this, hospitals should involve end-users in the system’s design, tailoring it to complement, rather than complicate, clinical practices (Sittig et al., 2008).
3. Data Discrepancies: Patient care was delayed because of disputes between internal EHRs and Health Information Exchange (HIE) records. Implementing standardized data-sharing protocols and enhancing interoperability between systems can mitigate this issue (Vest & Gamm, 2010).

3. A Patient-Centered Approach
If CDSS is to actually enhance healthcare, it must include a patient-centered methodology. By utilizing patient-generated data, including as lifestyle choices, self-reported symptoms, and wearable device metrics, CDSS can provide more custom-made recommendations. For instance, if Mr. Smith's wearable device had been linked to the hospital's system, early warning signs of pulmonary issues would have been revealed sooner.
Involving patients in collaborative decision-making also increases trust and adherence to treatment plans. CDSS can be designed to provide patient-friendly explanations of diagnoses and recommended treatments, empowering individuals to take an active role in their healthcare journey (Tcheng, 2017).

Conclusion
The experience of Dr. Carter highlights how innovative CDSS might be in modern-day medicine. When carefully applied, CDSS improves patient safety, advances workflows, and improves clinical decision-making. To optimize its advantages, among the problems hospitals face workflow interruptions, and variable data. A future where technology and human knowledge collaborate to provide the best possible healthcare results can be covered by CDSS by promoting a patient-centered approach.

References
Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. *BMC Medical Informatics and Decision Making, 17*(1), 36 https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-017-0430-8?utm_source=chatgpt.com
Sittig, D. F., Krall, M. A., Dykstra, R. H., Russell, A., & Chin, H. L. (2008). A survey of factors affecting clinician acceptance of clinical decision support. *BMC Medical Informatics and Decision Making, 8*, 6.
https://pmc.ncbi.nlm.nih.gov/articles/PMC1403751/?utm_source=chatgpt.com

Vest, J. R., & Gamm, L. D. (2010). Health information exchange: Persistent challenges and new strategies. *Journal of the American Medical Informatics Association, 17*(3), 288–294.
https://pmc.ncbi.nlm.nih.gov/articles/PMC2995716/
Tcheng, J. E. (Ed.). (2017). Optimizing strategies for clinical decision support: Summary of a meeting series. *National Academy of Medicine*.
https://www.nature.com/articles/s41746-020-0221-y?utm_source=chatgpt.com

Navigating the Labyrinth: How Clinical Decision Support Systems Can Light the Path to Better Healthcare (and Where They Can Get Lost)

The healthcare landscape is complex, a labyrinth of information, diagnoses, and treatment options. In this intricate maze, clinicians strive to provide the best possible care, but the sheer volume of data and the pressure of time can make navigating this terrain challenging. Enter Clinical Decision Support Systems (CDSS), a promising tool designed to illuminate the path to better healthcare outcomes. But like any compass, CDSS has its limitations, and its effective implementation requires careful consideration.  

CDSS, as highlighted in our recent case study [ Insert Case Study Title Here ], can significantly enhance clinical decision-making. These systems, which integrate patient-specific data with evidence-based guidelines and knowledge bases, can offer clinicians valuable insights at the point of care. For example, the case study illustrated how a CDSS alerted a physician to a potential drug interaction, preventing a serious adverse event. This exemplifies one of CDSS’s greatest strengths: improving patient safety by reducing medical errors (Berner & Detmer, 2001). Furthermore, CDSS can promote adherence to best practices, as seen in the case study's example of the system prompting guideline-concordant care for a patient with diabetes. By providing timely reminders and recommendations, CDSS can nudge clinicians towards evidence-based practices, leading to more consistent and effective care (Kawamoto et al., 2005).  

However, CDSS is not a panacea. Our case study also revealed some of its limitations. One challenge is alert fatigue. If the system generates too many alerts, especially irrelevant ones, clinicians may become desensitized and ignore them, potentially overriding critical warnings (Kushniruk & Patel, 2010). Another limitation is the potential for bias in the algorithms or data used by the CDSS. If the system is trained on data that reflects existing disparities in healthcare, it may perpetuate these biases in its recommendations. For instance, if the CDSS is primarily trained on data from a specific demographic, it may not perform as accurately for patients from other populations.  

To effectively harness the power of CDSS while mitigating its risks, we must address these challenges proactively. First, alert fatigue can be tackled by refining the system's algorithms to reduce the number of irrelevant alerts. This requires careful tuning and customization of the system to the specific clinical context. Second, addressing bias requires careful attention to the data used to train the CDSS. Efforts should be made to ensure the data is diverse and representative of the patient population the system will serve. Furthermore, ongoing monitoring and evaluation of the CDSS are crucial to identify and correct any biases that may emerge.  

A truly effective CDSS must also be patient-centered. Our case study highlighted the importance of incorporating patient input into the decision-making process. CDSS can facilitate this by providing patients with access to their own health information and allowing them to share their preferences and values with their clinicians. For instance, a CDSS could present patients with different treatment options, along with information about the risks and benefits of each option, and allow them to express their preferences. This shared decision-making approach empowers patients to actively participate in their care and leads to better patient satisfaction and outcomes (O'Connor, 2006).  

In conclusion, CDSS holds immense potential to transform healthcare by improving clinical decision-making, enhancing patient safety, and promoting personalized care. However, realizing this potential requires careful attention to the challenges of alert fatigue, bias, and the need for patient-centered design. By proactively addressing these issues, we can navigate the labyrinth of healthcare with greater confidence, using CDSS as a guiding light towards a future of more effective and equitable care.  

References

Berner, E. S., & Detmer, D. E. (2001). The role of cybernetics, artificial intelligence, and decision-support systems in clinical medicine. Journal of the American Medical Informatics Association, 8(2), 109–114.

Kawamoto, K., Cios, K. J., Dymek, M. P., Lin, F., & Glassman, P. A. (2005). Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.

Kushniruk, A. W., & Patel, V. L. (2010). Cognitive and human factors in the design and implementation of clinical decision support systems. Journal of the American Medical Informatics Association, 17(2), 121–125.

O'Connor, A. M. (2006). Using patient decision aids to improve the quality of healthcare.

"Smart Medicine: How CDSS is Revolutionizing Clinical Decisions"
The Role of CDSS in Clinical Decision-Making:
Clinical Decision Support Systems (CDSS) are designed to assist healthcare providers in clinical decision-making by providing evidence-based knowledge, patient data, and other important information. CDSS gathers data for electronic health medical records and works closely with IT to decipher vital information for the healthcare team. CDSS can alert clinicians of potential drug allergies, interactions, or abnormal lab results that may not be noticed in a busy clinical/hospital setting. These alerts are vital and essentially save lives and improve patient safety in a hospital emergency setting where there is a high probability of errors when healthcare professionals may be short-staffed or burnt out. In healthcare, the most common type of error is medication error, "One of every three adverse drug events (ADEs) precipitated by a medication error occurs when a nurse administers medication to a patient" (Hughes & Ortiz, 2005). In the case of Mr. Smith, when Dr. Carter entered the patient's history into the HER system, the CDSS generated an alert highlighting the patient's medication may interact adversely. This alert assisted Dr. Carter in making decisions about the next course of action. As Dr. Carter discovered in Mr. Smith's case, there are still some limitations, such as data discrepancies between providers and internal EHRs, which may cause delays and poor patient outcomes.
Challenges and Solutions:
Challenge: Workflow disruption was one of the challenges clinicians brought up to Dr. Carter in Metro Health Hospital. CDSS notifications may overwhelm clinicians, and they may begin to ignore or dismiss important warnings, which can have consequences and compromise patient safety.
Strategy: CDSS alerts should be programmed based on urgency, and clinicians should be properly trained to interpret and respond to alerts properly. "Clinicians' workflow is affected by alert appearance or presentation" (Olusegun Olakotan et al., 2020). CDSS notifications should be minimized to only relevant and urgent notifications, and the presentation of the alerts should be designed to be easily read and interpretable by providers.
Challenge: Institutional issues: Some clinicians fear that CDSS may replace clinical judgment, as was addressed in the conclusion of the meeting conducted by Dr. Carter in the case study. "Clinical decision-making is a complex and dynamic process, requiring evidence and a degree of decision-maker autonomy and professional accountability" ( Brown et al., 2019). Some clinicians may be reluctant to accept the new technology.
Strategy: Data standardization should reduce inconsistencies, ongoing clinician training as the system is updated, and strong data governance policies to ensure accuracy.
Patient-Centered Approach: CDSS uses patient data to improve patient outcomes, such as personalized treatment plans, as demonstrated in Mr. Smith's case. CDSS allows patients to participate actively in their healthcare and enhance patient outcomes. Patient data is generated and can be shared with different providers to ensure timely treatment and prevent medication interactions. CDSS fosters patient engagement; patients who feel heard and considered in decision-making are more likely to follow the provider's treatment plan.

Brown, G. D., Pasupathy, K.S & Patrick, T. B (2019). Health Informatics: A Systems Perspective (second edition.). Health Administration Press.

Design for improved workflow. Design for Health. https://www.sciencedirect.com/science/article/abs/pii/B9780128164273000130 (2020, January 31).

Hughes, R. G., & Ortiz, E. (2005). Medication Errors. The American Journal of Nursing, 105(3), 14–24. http://www.jstor.org/stable/29746363

CDSS: Patients Over Patience

Clinical Decision Support systems (CDSS) assist clinicians in making better, more informed decisions faster than ever. CDSSs constitute a useful tool in reducing human related errors and facilitating evidence-based clinical decision making for medical doctors, pharmacists, and other health professionals (Detopoulou et al., 2023). These systems improve patient care by managing health information technology (HIT) and electronic health records (EHR). Over the past few decades, CDSSs have evolved to become both strategic and tactical tools that offer strategic benefit to healthcare organizations by tactically assisting clinicians during the patient care process (Brown et al., 2019). CDSS has evolved into a strategic clinical tool with various challenges to overcome, such as human factors, interoperability, free-text technology, and alert fatigue.

Challenges and Solutions

Human Factors
Humans are involved in the development, implementation, and utilization phases of CDSSs (Brown et al., 2019). CDSS is developed using the knowledge of clinicians and adapted to suit the needs of the people operating it. Larger institutions that have many people might face issues adapting CDSS or that they do not need it at all. Clinicians might have trouble trusting the information provided by the CDSS and whether the information is up to date. Human-computer interaction is also an issue if CDSS is not user-friendly to the point of frustration, input errors, and decrease in satisfaction.

The CDSS should be tested for its usability both before and after implementation, and feedback should be gathered to ensure an overall positive experience.

Interoperability
Healthcare interoperability is “the ability of different information technology systems and software applications to communicate, exchange data, and use the information that has been exchanged” (Brown et al., 2019). Not only is human interaction important, but the information needs to be simple and easy to disseminate with other systems. Maintaining effective and efficient interoperability is an issue today.

CDSS interoperability requires smooth communication that can be achieved by emphasizing the need to adapt a system in which different organizations involved can adapt their systems accordingly.

Free-Text Technology
Free text is a method of entering information into the EHR in an unstructured, natural language format (Brown et al., 2019). Entering information by hand can lead to human error or misunderstandings since clinicians might abbreviate, simplify, or enter information that can influence the data found in the CDSS.

Despite this, CDSS are being designed to process information in a standardized format and extract meaningful data from free-text information.

Alert Fatigue
CDSS is an effective support tool to help clinicians maintain the overall health of their patients by providing relevant information through reminders and alerts. However, alerts and notifications might overwhelm clinicians. Studies show that about 70 percent of primary care practitioners (PCPs) alerts were manageable, and physicians would override 49 percent to 96 percent of all safety alerts (Brown et al., 2019). This can lead to unintentionally ignoring alerts that could prevent realistic harm.

Reducing alerts but fine-tuning CDSS to only notify clinicians of high importance is crucial in both managing the health of patients as well as affecting the mental status of clinicians by overwhelming them.

Patient-Centered Approach

In Theory
Patient-Centered CDSS allows patients to get the help they need in a timely manner by having evidence-based information, analysis, and research in one convenient location. CDSS can quickly generate different diagnoses and diseases in relation to a patient’s symptoms and allows clinicians to make lifesaving decisions quicker than ever before.

In Practice
Dr. Emily Carter, an internist at Metro Health Hospital, shared a case where CDSS helped save the life of one of her patients by identifying her patient’s symptoms. The system suggested tests and guidelines that contributed to a life-saving intervention, demonstrating the real-world impact of CDSS on patient care.

Dr. Carter explains how there were concerns during implementation by her staff, expressing concerns about the excessive notifications and their autonomy being overridden by automated suggestions, Carter asserts the importance of refining CDSS as a support tool to improve the overall performance of clinicians.

While challenges like human errors, interoperability, free-text processing, and alert fatigue persist, ongoing refinements ensure CDSS remains an invaluable tool in modern medicine.



 
References

Brown, G. D., Pasupathy, K. S., & Patrick, T. B. (2019). Health informatics : a systems perspective (Second edition.). Health Administration Press.
Detopoulou, P., Papandreou, P., Papadopoulou, L., & Skouroliakou, M. (2023). Implementation of a nutrition-oriented clin

"A new line of thinking in healthcare"
Technology like Clinical Decision Support Systems (CDSS) is changing the way healthcare works, offering tools to help doctors and nurses make better decisions. Let’s take for example Dr. Emily Carter. Dr. Carter works at Metro Health Hospital and has seen the benefits of CDSS, which is connected to the hospital’s Electronic Health Records (EHR). This system provides evidence-based recommendations to help doctors diagnose, treat, and prevent health problems. It has proven incredibly useful, but there are also challenges that need to be solved for it to be better.
CDSS can help improve healthcare outcomes by offering real-time recommendations based on the latest medical research. For example, using Dr. Carter again, she treated John Smith, a 63-year-old patient with chest pain and shortness of breath. The system quickly provided possible diagnoses like acute coronary syndrome and pulmonary embolism. It also alerted Dr. Carter to potential issues with Mr. Smith’s medications and suggested tests like a D-dimer and an EKG, which were consistent with current guidelines. This kind of support helps doctors make faster and accurate decisions. In Mr. Smith's case, the system helped identify a pulmonary embolism, allowing Dr. Carter to start the right treatment immediately, which improved his condition. CDSS can reduce the amount of mistakes made, improve diagnoses, and ensure that patients get the right treatment much faster. However, CDSS has some limitations. The accuracy of its recommendations depends on data entered into the system. If the data is incomplete or wrong, then the suggestions may not be helpful. Also, while CDSS is helpful, it cannot replace a doctor’s judgment. It’s important for doctors to use the system as a tool, not a replacement for their experience and expertise.
While CDSS has many benefits, there are also challenges. One issue is “alert fatigue”—when doctors and nurses get so many notifications from the system that they start ignoring them. This can be incredibly dangerous if emergency alerts are overlooked. Some doctors also worry that relying too much on CDSS will reduce their ability to make decisions independently. As a solution to these issues, hospitals can adjust the system to reduce unnecessary alerts. For example, the system could prioritize more urgent alerts while allowing doctors to review less critical ones later, like a triage. Giving clinicians the option to adjust the frequency and types of alerts they receive can also help reduce distractions. Training is also key. Doctors need to understand how to use the system effectively and feel confident that it supports their work, rather than taking over it. Another challenge is that some nurses have difficulty navigating the system’s interface. To solve this, hospitals should provide training for all staff members and offer ongoing support. The system’s design should also be simple and straightforward to make it easier to use.
One of, if not the, primary goals of healthcare today is to focus on the patient. CDSS can help with this by considering each patient’s unique needs and issues, such as their medical history and lifestyle. In Mr. Smith’s case, the system factored in his conditions like diabetes and hypertension, which helped Dr. Carter make the right decision for him. Moreover, CDSS can help doctors explain diagnoses and treatment options to patients in an easy-to-understand way. This makes it easier for patients to be part of the decision-making process. This is important because when patients understand their care options and are involved in the process, they are more likely to follow through with treatment plans, leading to better outcomes.
In conclusion, CDSS has the potential to improve both the speed and quality of treatment in healthcare, as shown in Dr. Carter’s experience. However, to make it work better, it’s important to solve challenges like alert fatigue, system usability, and ensuring that the system supports, rather than replaces, a doctor’s judgment. When CDSS is used in a way that includes both the clinician’s expertise and the patient’s needs, it can lead to more personalized and effective care, ultimately improving our healthcare system.

Healthcare's Frenemies: The Love-Hate Relationship with CDSS

The Role of CDSS in Clinical Decision-Making
Imagine providers having the ability to make faster, more accurate decisions which could potentially save countless lives. This is the reality brought to us by Clinical Decision Support Systems (CDSS). Clinical Decision Support Systems (CDSS) can improve healthcare outcomes by offering real-time, evidence based clinical information to assist healthcare providers make well informed decisions, quickly. Some examples of CDSS in use are via drug interactions, preventive care screenings and duplications of drugs and labs, management of chronic conditions and offering differential diagnoses based on a patient's symptoms, which was the case in the example provided in the case Study. The case study provided of Dr. Emily Carter gave a best case scenario example involving a 63 year old patient, who benefited from the use of CDSS, and potentially saved his life with a pulmonary embolism diagnosis. Even with all CDSS can do, and the progress it has made, CDSS is not without its limitations.


Challenges and Solutions:
It is important to remember that the keyword with CDSS is “assist”. CDSS is not meant to replace or override the decision making of the experienced providers, however not all providers see it this way as some are very reluctant to the technology, thus human factors are a challenge for CDSS. Trust and credibility are one of the contributing factors causing concern with providers. To combat this challenge, it is important to ensure that the CDSS data bases are regularly updated as new clinical guidelines are available (Brown et al., 2019).
Another human factor posing challenges is simply lack of computer knowledge and training of some providers. A lot of doctors are used to the days of paper charting, and struggle with organization implementation of EHR’s, therefore CDSS needs to be designed to be user friendly for all types of users from young to old. CDSS needs to be easy to view, read and understand (Brown et al., 2019).

Another challenge for CDSS is Alert Fatigue. Providers can become overwhelmed, especially when they are rushing and have a lot of patients waiting for them, therefore they begin to just click anything to close the alert, without always reading the message. As our textbook mentions, 70% of PCP’s are reporting more alerts than they can handle and may unintentionally override a critical alert which is a huge concern. To combat this, I feel that alerts should be tied to the specific user logged in. There are some alerts only a nurse would need to see, and some only administration or other staff needs to see and can address. The providers however should only get alerts which are pertinent to the care they are providing to a specific patient and are indeed crucial and critical to the patients outcome.

Patient-Centered Approach
It is important to involve patients in the decision making process so that they can feel a sense of ownership in their health outcomes. Tailoring treatments to individual by using their genetic information, environmental and lifestyle factors, CDSS can assist providers to identify the most effective therapies for each patient, therefore minimizing adverse effects and improving patient outcomes (Chen et al., 2023).

Gordon Brown. (2019). Health Informatics: A Systems Perspective, Second Edition: Vol. Second edition. AUPHA/HAP Book
Chen, Z., Liang, N., Zhang, H., Li, H., Yang, Y., Zong, X., Chen, Y., Wang, Y., & Shi, N. (2023, November 28). Harnessing the power of Clinical Decision Support Systems: Challenges and opportunities. Open heart. https://pmc.ncbi.nlm.nih.gov/articles/PMC10685930/

“CDSS and Strides Towards Smarter Healthcare”

Role of CDSS in Clinical Decision-Making:
The integration of technological advances has ignited a shift towards more efficient and effective healthcare. The use of data-management tools such as Clinical Decision Support Systems (CDSS) has enabled physicians to better analyze patient data, create personalized treatment plans, have better diagnostic accuracy, and improve healthcare quality. CDSS can assist physicians in decision-making processes through the use of data from prior assessments, existing work-flows, and healthcare theories (Wasylewicz & Scheepers-Hoeks, 2018). Integrating CDSS is an opportunity to better patient outcomes, particularly as healthcare becomes increasingly complex. With the widespread use of Electronic Health Records (EHR) and coordinated care systems, healthcare organizations now have the ability to access vast amounts of data enabling physicians to use that information as seen in the case of Dr. Carter and Mr. Smith. Through the use of CDSS, data is examined to help physicians produce diagnostic recommendations, or prompt alerts for concerns. From a business perspective, the use of CDSS can also better analyze an organization’s financial health and utilization methods.

Challenges:
While CDSS brings an array of benefits, it also possesses limitations. Implementing CDSS can be difficult due to a lack of interoperability with an organization’s existing EHR system. This is because CDSS can function as stand-alone systems or exist in systems that do not function with each other. Additionally, some organization’s health information systems (HIS) lack the infrastructure to support CDSS. There are various organizations who are still working to fully integrate into EHR systems that are aligned with others in the field.

Another challenge also seen in the case with Dr. Carter and Mr.Smith, is that CDSS may be dependent on users’ computer literacy. Nurses were confused on how to use the system’s interface. Therefore, we must take into consideration that the workfield consists of people from varying ages, some of whom are not as technical savvy.

Solutions:
The advantages of CDSS may outweigh the challenges accompanying it. To alleviate the challenge of interoperability, healthcare organizations should conduct thorough research and a well-rounded analysis of their HIS. Given that governing agencies and medical organizations are actively advocating for interoperability standards to be utilized, it is vital for organizations to determine what updates they may need to improve to their HIS system. A possible solution would be the utilization of cloud EHRs which have more flexibility to connect to other systems like CDSS. (Sutton et al., 2020).

To address the concern of computer literacy, organizations must determine a baseline of users’ technological skills (Sutton et al., 2020). They should prioritize teaching the basic fundamentals of CDSS and plan continuous training in increments so that it does not disrupt current workflows.

Patient Centered Approach:
Healthcare providers should harness the power of CDSS especially as resources to improve patient care and business operations become more accessible. To foster a patient-centered approach, it would be beneficial to include them in decision-making processes by having open discussions about their diagnoses and preferences. Through meaningful dialogues, physicians can bridge the gap between patient concerns and effective treatment plans. It will also be essential to take into account the social determinants that may affect a person’s health. This, along with other patient information can better instruct physicians of the best care route. Facilitating patient and physician clinical exchange, incorporating data on social determinants of health, and closely engaging patients in their own care plans are just a few tools that can enable patient centered CDSS.


References:

​​Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for Success. Npj Digital Medicine, 3(1). https://doi.org/10.1038/s41746-020-0221-y

Wasylewicz, A. T., & Scheepers-Hoeks, A. M. (2018). Clinical Decision Support Systems. Fundamentals of Clinical Data Science, 153–169. https://doi.org/10.1007/978-3-319-99713-1_11

“Alexa, Diagnose My Patient”

In an era where technology is holds the reins, Clinical Decision Support Systems (CDSS) are like the GPS of healthcare—offering directions, avoiding roadblocks, and sometimes yelling at you when you're about to make a questionable turn. As stated, “CDSSs are used in a clinical setting to help physicians make decisions to improve clinical performance and patient care (Muhiyaddin et al, 2020). At Metro Health Hospital, Dr. Emily Carter embraced this technological co-pilot; however, she quickly realized that not everyone was thrilled about their new automated assistant.

The Role of CDSS in Clinical Decision-Making

CDSS is designed to make physicians look like medical fortune tellers as it helps in predicting potential diagnoses, flagging dangerous drug interactions, and reminding them about reports they probably should have reviewed already. In Dr. Carter’s case, her patient, Mr. John Smith, presented with chest pain and shortness of breath. The system quickly generated a list of differential diagnoses. It also recommended an EKG and provided an urgent reminder about potential medication interactions. Thanks to this digital nudge, Dr. Carter identified a pulmonary embolism in record time and initiated life-saving treatment. This may sound like an ideal concept; however, the reality is dealing with an incessant stream of notifications. Physicians quickly hit their limit with constant alerts, leading to what’s known as alert fatigue (Anker et al, 2017). Additionally, some clinicians feel like their years of training are being questioned by a machine, making CDSS a double-edged scalpel—useful, but sharp enough to cut into clinical autonomy.

Challenges and Solutions

One of the biggest complaints at Metro Health was that CDSS notifications were as relentless as a needy ex—constantly popping up and disrupting workflow. If every minor issue triggers an alert, even the most well-intentioned reminders get tuned out. A smarter approach is to introduce tiered alerts that separate the “This will kill your patient” warnings from the “Just a heads-up” notifications. Allowing customization can also help, so doctors can set their own thresholds for what deserves a red flag.
Another issue was workflow interference. If using CDSS feels like solving a CAPTCHA every time you try to order a test, no one’s going to be happy. A well-integrated system should feel like an extension of the EHR rather than an obstacle course. Clinician input during the design process can prevent CDSS from becoming a frustrating extra step and instead turn it into a seamless part of patient care.
Lastly, remains the classic problem of mismatched data between Health Information Exchanges (HIEs) and internal EHRs. When records fail to sync properly, delays occur and doctors are left with outdated or conflicting information. These inconsistencies can disrupt clinical workflows and undermine confidence in the data; thus, complicating the decision-making process. The best fix? Standardized data protocols and real-time validation tools to ensure that what is on-screen actually reflects reality.

A Patient-Centered Approach to CDSS

CDSS might be built for clinicians, but that does not mean patients should be left out of the equation. Integrating patient-reported data like symptoms, medication adherence, and lifestyle habits can make recommendations more tailored and relevant. For example, if Mr. Smith had been able to log his symptoms in a patient portal ahead of his visit, this would have allowed the system to generate more precise insights even before Dr. Carter walked in.
Shared decision-making is another area where CDSS can shine. According to Dullabh et. al (2024) “Most EHRs currently lack the infrastructure needed to receive, store, and display patient-generated health data that are easily accessible and interpretable by clinicians, which prevents integration into clinician workflows.” Instead of simply presenting doctors with a list of options, why not create patient-friendly versions that outline risks, benefits, and alternative treatments? This way, patients feel involved rather than just nodding along while their doctor stares at a screen. We also need to consider the importance of health literacy. Medical jargon may sound impressive; however, if patients are unable to understand their care plans, they are much less likely to adhere to treatment. A well-designed CDSS should translate complex recommendations into plain language so patients can actually follow them.

In all, CDSS has the potential to transform clinical decision-making. It can help make healthcare delivery faster and more precise; however, without careful implementation, it can just as easily turn into an overbearing backseat driver. When used wisely, these systems can do what they were designed to do: help doctors save lives, without driving them crazy in the process.


References
Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., Kaushal,

"Optimizing Clinical Decision-Making with CDSS: Benefits, Challenges, and Patient-Centered Care"

Clinical Decision-Making and the Function of CDSS Clinical Decision Support Systems (also known as CDSS) are essential to contemporary healthcare because they help physicians make evidence-based decisions. After analyzing patient data, these systems offer suggestions for diagnosis, care, and prevention. Through the identification of possible drug interactions, the recommendation of suitable diagnostic procedures, and the integration of external health data for a thorough evaluation, CDSS can improve clinical decision-making, as was shown in the example of Dr. Emily Carter and Mr. John Smith.

Improvement of Healthcare Outcomes and Limitations
The results of healthcare could be greatly enhanced with CDSS. Research shows that CDSS can improve diagnostic precision, decrease medication errors, and encourage adherence to clinical recommendations (Berner, 2009). In Mr. Smith's case, the system detected a potentially fatal pulmonary embolism, which prompted quick treatment.CDSS does, however, have certain drawbacks. Alert fatigue is a significant issue, as too many messages cause doctors to ignore important cautions (Ancker et al., 2017). Furthermore, there is a chance that clinical autonomy may be compromised by an excessive dependence on computerized advice. Furthermore, delays in patient care may result from integration problems, such as differences between internal Electronic Health Records (EHRs) and Health Information Exchange (HIE) records (Sutton et al., 2020).

Patient-Centered Methodology
Patient feedback should be incorporated into CDSS to improve individualized care. A more comprehensive approach to treatment is made possible by including patient-generated health data into the system, such as wearable device metrics and self-reported symptoms (Wright et al., 2018). Furthermore, patients can receive customized treatment alternatives using shared decision-making tools in CDSS, guaranteeing that their values and preferences are taken into account while making medical decisions.CDSS can continue to develop as a useful tool in clinical decision-making by tackling these issues and implementing patient-centered techniques, which will ultimately result in better healthcare outcomes.

Challenges and Solutions:
Solution: Integration with healthcare operations can be streamlined by adjusting alert thresholds and improving user interfaces. According to Middleton et al. (2016), offering focused training sessions can also aid users in navigating the system more effectively.
Alert Fatigue: Clinicians may become insensitive to critical alerts if they receive too many notifications.
Solution: This problem can be lessened by putting in place tiered alert systems, which prioritize vital notifications while reducing non-urgent ones (Ancker et al., 2017).
Data discrepancies: Inconsistencies and delays may result from differences between HIE and EHR data.

Conclusion
Clinical Decision Support Systems (CDSS), which provide prompt, evidence-based recommendations, have enormous potential to improve healthcare outcomes. However, issues like data inconsistencies, alert fatigue, and workflow disruptions must be resolved for the adoption to be successful. Healthcare organizations can optimize the advantages of CDSS by improving system interfaces, giving priority to urgent warnings, and guaranteeing data interoperability. A more individualized approach to care is also promoted by including patient participation in the decision-making process. Continuous advancements in CDSS will be essential to maximizing clinical effectiveness and patient outcomes as technology develops.


References

Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Medical Informatics and Decision Making, 17(1), 36. https://doi.org/10.1186/s12911-017-0430-8
Berner, E. S. (2009). Clinical decision support systems: State of the art. Agency for Healthcare Research and Quality (US).
Middleton, B., Sittig, D. F., & Wright, A. (2016). Clinical decision support: A 25-year retrospective and a 25-year vision. Yearbook of Medical Informatics, 25(S 01), S103-S116. https://doi.org/10.15265/IYS-2016-s034
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17. https://doi.org/10.1038/s41746-020-0221-y
Wright, A., Feblowitz, J. C., Pang, J. E., Shiffman, R. N., & McGowan, J. (2018). Use of a decision support system to improve adherence to national asthma guidelines. Journal of the American Medical Informatics Association, 25(4), 412-418. https://doi.org/10

The Revolutionary Approach of CDSS

1. The role of Clinical Decision Support Systems in Clinical Decision-Making is to help healthcare personnel come up with informed decisions when it relates to a patient's health. In the case study provided, Dr. Emily Carter, an internist at Metro Health Hospital, must come up with a diagnosis for a patient that presents to the Emergency Room with chest pain and shortness of breath. However, with the help of CDSS, the system "draws from a vast reservoir of scientific evidence" for Dr. Carter to eventually come up with a diagnosis (Brown et al., 2019, p. 23). In the case study, CDSS was able to help Dr. Carter learn that the symptoms and aligned with pulmonary embolism; because she was able to reach this decision, the successful treatment of anticoagulation therapy was able to stabilize the patient. Overall, the role of CDSS is to provide evidence-based guidelines and to helping doctors and nurses reach certain diagnoses.

2. Challenges and Solutions
Certain challenges related to the implementation of Clinical Decision Support Systems in healthcare settings may sometimes not even be technological issues, but rather people issues. One such challenge is that of human resistance to change. "Such concerns will affect acceptance by health professionals" because many people may not adapt well to the new systems (Brown et al., 2019, p. 27). Change can be scary, especially when humans and institutions stuck to the same systems for so long. CDSS has been developed to help healthcare professionals reach decisions about patients' health, however some may believe that CDSS may even slow down the decision-making process. However, to avert this challenge, one solution would be to bring up "clinical evidence" to health professionals to show them that these systems are designed to help them (Brown et al., 2019, p. 28). In healthcare, evidence is essential in choosing what type of treatment is better for a patient; however, in this technological matter, health informatics should be backed with evidence in order to ease the worries of providers everywhere.

Another challenge is integration and interoperability. Integration involves putting things together, meanwhile interoperability is the ability to "exchange information and to use the information that has been exchanged;" so in essence, the challenge to CDSS is whether or not the systems have easy usability and accessibility (Bates & Samal, 2018). Many employees are often resistant to change because of fears that new systems may be harder to learn; however, the point of health informatics is to make systems more efficient and less overwhelming. Going to the example of Dr. Carter, many colleagues became fatigued due to the amount of notifications; however, forming a CDSS that "only generates clinical alerts selectively" would make for an excellent solution for this interoperability issue (Bates & Samal, 2018). Too much information only overwhelms people.

3. CDSS can incorporate patient input to enhance personalized care by providing more access of patient records and health recommendations to patients themselves. Often times, when a patient is diagnosed with an issue or disorder, a physician would typically provide specific recommendations to better their care. However, this often leads patients to not understand the medical jargon, or they do not know which option to choose. So, CDSS would allow for more personalized care to patients themselves if they were able to see the list of health recommendations, and they can ideally research which decision would be best for them. Instead of listening to one or two health professionals' recommendations, having that easy access to research would be very beneficiial.

Bates, D. & Samal, L. (2018). Interoperability: What is it, how can we make it work for clinicians, and how should we measure it in the future? Health Services Research. 11;53(5):3270–3277. doi: 10.1111/1475-6773.12852

Brown, G. D., Pasupathy, K. S., & Patrick, T. B. (Eds.). (2019). Health informatics: A systems perspective (2nd ed.). Health Administration Press.

Goodbye “Dr. Google”! Hello Clinical Decision Support Systems.

The Role of CDSS in Clinical Decision-Making:
Clinical Decision Support Systems have many positive and negative effects on patients, healthcare providers, and healthcare organizations. One way that CDSS enhances healthcare outcomes is through real-time and evidence-based recommendations. For example, when Mr. Smith presented to the ED with chest pain and shortness of breath, the CDSS system alerted Dr. Carter of drug interactions and test recommendations within a short period of time. Dr. Carter was able to use that information and quickly treat Mr. Smith, further preventing a negative patient outcome. Another way that CDSS positively impacts patient outcomes is through the reduction of human error. As we have learned in many undergraduate and graduate-level health administration classes, humans are designed to make mistakes, which has unfortunately led to a significant number of medical error incidents. CDSS reduces diagnostic errors, which leads to compliance with clinical guidelines and optimizes treatment plans.
On the other hand, CDSS has some shortcomings. In the case study, several physicians expressed concerns about their autonomy being overridden by automated suggestions from the CDSS. According to the NIH, data privacy, system integration, and clinician acceptance is a concern. To combat this, the article suggests that strong “collaboration between stakeholders and investment in CDSS development and evaluation” would help the integration of CDSS. (Chen et al., 2023) Another major limitation of integrating CDSS into clinical practice is cost and infrastructure. Depending on the size of the practice implementing CDSS can range from thousands to millions of dollars and requires extensive IT resources to fully integrate. Additionally, what comes with increased data sharing also comes with legal and ethical concerns. For example, if a clinician’s medical decision is overridden by the CDSS and causes a less favorable patient outcome, who is at fault at the expense of the patient?

Challenges & Solutions:
Here are some challenges mentioned in the case study and solutions for how to combat each issue:
Alert Fatigue: Clinicians complained of too many alerts, causing clinicians to become desensitized to the alerts no matter how serious the alert is.
Solution: According to Health informatics: A systems perspective, one solution could be to redesign the alert system. For example, administrators could redesign the system to only include alerts that are “high significance, personalizing the alerts to fire only in specific clinical settings, and categorizing alerts according to levels of importance depending on the clinical significance”. (Brown et al., p. 133) This would regain the engagement of clinicians and decrease alert fatigue.
Workflow Disruptions: Since it is very difficult to implement change, some clinicians noted that the CDSS disrupted their established routines.
Solution: Administrators should reevaluate the CDSS algorithms and workflows to see how the current flow is disrupting the clinicians' workflow. The goal of CDSS is to seamlessly integrate the CDSS algorithms into the current workflow to enhance patient outcomes and workflows, so administrators must ensure that CDSS is not a hindrance to what is already set in place.
Usability Issues: Nurses with Metro Health expressed dissatisfaction with the navigation of the CDSS. The usability issues have caused operational inefficiencies for these nurses.
Solution: Consulting IT and UI/UX designers may help with redesigning the CDSS so that it is user-friendly for the nurses to use. This would also aid the adoption of change and decrease the number of mistakes made by users.
Data Integration Issues: There have been issues with data integration because there are discrepancies between HIE and EHR records, causing delays in cases. According to a blog from Gradiant, a technological solution developer, having consistent data at an HCO is important because “when data are presented on a consistent basis, no matter what the source, it is easier for practitioners to quickly get to the bottom of the issue as they make treatment decisions.” (Castro, 2017)
Solution: The IT department must strengthen interoperability between systems to ensure smooth information exchange and reduce data errors. Gradient mentions that interoperability in CDSS is extremely important because it can improve efficiency, ensure safer care transitions, and help lower costs.

Patient-Centered Approach:
Patient input is crucial for creating patient-centered and personalized care. In fact, CDSS, uses patient portals, patient-reported outcomes, symptom management, wearable devices, and patient satisfaction surveys to monitor patient health in real time. This allows healthcare providers to make on-the-fly decisions about a patient’s treatment plan. In the case of Mr. Smith, CDSS would greatly benefit his health

Balancing Technology and Clinical Judgment: Enhancing Decision-Making with CDSS

The Role of CDSS in Clinical Decision Making
Clinical Decision Support Services (CDSS) is an extremely important tool, transitioning the way care is delivered around the world. CDSS is used to provide real-time, evidence-based recommendations for treatments and preventative care. This is monumental as it can help physicians see things that they might not have caught at first glance or cannot even detect. The system is able to assist healthcare providers by integrating patient history, lab results and practice guidelines. Integrating these three crucial pieces of information assists healthcare providers and reduces the space for error. It also assists physicians in the ultimate diagnosis of a patient based on symptoms and prior medical history, as seen in our Case Study in Dr Carter’s case. Dr. Emily Carter’s experience demonstrates both the benefits and challenges of CDSS implementation. In her case, “the system quickly generated a list of differential diagnoses, flagged a potential drug interaction, and recommended immediate tests based on evidence-based guidelines” (Metro Health Hospital Case Study, 2025). This is something that healthcare professionals have never had the opportunity to interact with, and CDSS is showing to be an essential tool to prevent errors. However, CDSS does come with limitations such as alert fatigue and data reliability concerns. Excessive notifications can strain physicians’ ability to respond and discrepancies within systems can lead to inaccuracies.

Challenges and Solutions
The first challenge I will be discussing is healthcare provider alert fatigue. Alert fatigue is a physicians’ lack of response to an alert due to the constant alerts they have going off such as pagers, messages and phone calls. However, I think that there are ways for us to address such fatigue. The first approach I would use is to customize alert sounds, with the sound for an extremely critical alert being extremely loud and rare. This would ensure that all people, doctors and care partners, acknowledge the alert due to its severity and uniqueness. Next, I would remove any redundant alerts. It is pretty common to be receiving redundant alerts and assigning a task force to search for and remove them would greatly benefit the provider’s peace of mind.

The next challenge is when a CDSS workflow is disrupted. While the implementation of a CDSS system is aimed at reducing inefficiencies and streamlining processes, it might do the opposite for providers. Doctors and nurses are accustomed to following strict protocol for their activities, and any change will disrupt their current processes. In Dr. Carter’s case, some clinicians reported that CDSS alerts and recommendations interrupted their thought process and slowed down decision-making, particularly when multiple alerts required attention.

Patient Centered Approach
A well-designed Clinical Decision Support System (CDSS) should not only assist clinicians but also enhance patient-centered care by incorporating patient input and preferences. One way to achieve this is by integrating patient-reported symptoms and lifestyle factors into the system. For example, a CDSS could allow patients like Mr. Smith to update their medication adherence, diet, or recent symptoms via a mobile app, ensuring that clinicians have the most up-to-date information when making treatment decisions. Additionally, CDSS can facilitate shared decision-making by presenting risk-benefit analyses of different treatment options in a patient-friendly format, empowering individuals to take an active role in their healthcare. In Dr. Carter’s case, a patient-facing summary of Mr. Smith’s treatment plan could have improved his understanding and engagement in managing his pulmonary embolism.

References:
Berner, E. S. (2020). Clinical decision support systems: State of the art. Agency for Healthcare Research and Quality (AHRQ). https://www.ahrq.gov
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. npj Digital Medicine, 3, 17. https://doi.org/10.1038/s41746-020-0221-y
Baysari, M. T., Duong, M. H., Hooper, L., & Henderson, S. B. (2021). Alert fatigue in clinical decision support systems: Challenges and mitigation strategies. Journal of the American Medical Informatics Association, 28(3), 463–469. https://doi.org/10.1093/jamia/ocaa261

THE FUTURE OF COLLABORATIVE HEALTH CARE

1. The Role of CDSS in Clinical Decision-Making
Health informatics is essential for helping clinicians make the most accurate decisions in patient care. In the podcast, Informatics: Data & Decision Making in Health Care, Dr. Stan Huff mentions that computer algorithms have demonstrated the ability to sometimes outperform individual physicians and panels of expert physicians in certain decision-making situations. Dr. Huff goes on to suggest that as technology evolves, computer programs will become increasingly skillful at diagnosing, predicting, and managing health outcomes. Some of the benefits Dr. Huff cites for in-data decision making include computers offering distinct advantages over human clinicians by avoiding fatigue and confusion, processing large amounts of data, and accessing broader knowledge. Furthermore, the text supports this idea as it states, “in a well-designed and well-managed system, there will be a loss of individual decision control as decisions are increasingly based on collaboration and informed by accumulated clinical evidence (Brown, 2019, p. 50).” This means clinical decision-making will become more collaborative and evidence-based, requiring healthcare providers to work alongside AI systems to provide the best care for their patients. However, the integration of health informatics also presents challenges, such as potential loss of individual decision control, the need for healthcare providers to work alongside AI systems, limitations in current guidelines and measurements, disparities in adoption between large hospitals and smaller clinics, and concerns about the rigor of studies reporting positive outcomes from Clinical Decision Support Systems (CDSS) (Brown, 2019).

2. Challenges and Solutions
The implementation of the CDSS in the case study presented several challenges for Dr. Carter and the clinical staff. Alert fatigue emerged as a significant issue, with staff complaining about an overwhelming number of notifications. To avoid such fatigue, CDSS should be customized to prioritize critical alerts and filter out non-essential information to allow clinicians to focus on what is most relevant. A second challenge expressed by clinicians was concern that their professional autonomy would be compromised by automated suggestions and disruption of their traditional workflow (e.g., nurses faced difficulties navigating the system’s interface). One way to address this reluctance may be through comprehensive training aimed at changing mindsets and fostering trust through education on the benefits, limitations, and most effective use of CDSS. Finally, a third challenge mentioned in the case study was data discrepancies between Health Information Exchange (HIE) records and Electronic Health Records (EHRs), leading to delays in some patient cases. This issue can be potentially be countered through the need for constant updating in technology, similar to keeping a laptop's software current. These challenges collectively emphasize the complexity of integrating new health informatics systems and the importance of addressing user needs, balancing automation with clinical expertise, providing adequate training, and ensuring data accuracy.

3. Patient-Centered Approach
CDSS can incorporate patient input to enhance personalized care by allowing patients to play an active role rather than a passive one. For example, the text provides an example from the University of Missouri Health Care (2018) and how they developed a smartphone application that allows patients with depression to enter their moods and symptoms into a log and share data with their psychiatrists. By sharing this data with their psychiatrists, patients provide valuable insight into their daily health baseline that might otherwise be forgotten or overlooked during infrequent office visits. This active role not only empowers patients to track their medical history and monitor their health, but also offers providers with complete, real-time data to inform treatment decisions. This is just one example, but I imagine this type of personalization can be applied to the management of other conditions such as diabetes, hypertension, or asthma.

References
Brown, G. D., Pasupathy, K. S., & Patrick, T. B. (2019). Health informatics: A systems perspective (Second edition). Health Administration Press.

Weyn, T. (Host). (2020, December 29). Informatics: Data & Decision-Making in Healthcare. In Heroes of Healthcare. https://open.spotify.com/show/5ZXKezlIQctP8QDXOyWh4N

Everything uses AI technology



We have made it to a time in our lives where technology is something that is used on an everyday basis. With that being said their Health care systems use CDSS (Clinical Decision support systems). These systems are here to help providers make better clinical decisions. In this case study Dr. Carter used this CDSS system to diagnosis her client and give him the treatment that he was needed. But when the information was presented to her staff, they weren’t too happy about it as some of them were having issues with the system.

The Role of CDSS in Clinical Decision-Making:


What CDSS is here to do for our health care provider is to help make the decision on a diagnosis for a clinical. With that being said Dr. Carter was able to put Mr. Smith’s symptoms into the CDSS system and they were able to provide what Dr. Carter needed to do to make him better. Dr. Carter was alerted about the client’s medication and not to interact other medications with the medication that he is taking now. CDSSs are now essential to clinicians for accessing the best evidence and incorporating it into decision making process.

Challenges and Solutions:

When it comes to the CDSS there can be many different challenges that healthcare providers can face. Just how Dr. Carter’s staff felt like it disrupted their workflow it can happen to many other clinicians. Another challenge would be that staff are being confused about the alerts that are popping up in the system. I think that the solution for this would be a training for staff from Dr. Carter so that staff are clear about what they are using and simply how they can use it.

Patient-Centered Approach:

One thing that is nice about the CDSS is that it is not only for the clinician it can be for the patient as well. Having the patients input or feedback on things can be very important. In this case if Mr. Smith wasn’t comfortable with the diagnosis or treatment that he was receiving he could have explained to the doctor that he felt like that diagnosis was correct.


As a whole CDSS can make better decisions for providers and make things a little easier for them. Being able to put a client’s symptoms in the system can go a long way as the provider may see test that need to be done that they have missed. I think that as a patient it is nice to be able to give your input within your treatment.

Reference:
Brown, G. D., Pasupathy, K. S., & Patrick, T. B. (Eds.). (2019). Health informatics: A systems
perspective (2nd ed.). Health Administration Press.

Brighter Healthcare: Improving Care with CDSS

Clinical Decision Support Systems (CDSS) offer evidence-based recommendations to assist healthcare providers in making informed clinical decisions. These systems have transformed healthcare by enhancing patient outcomes and minimizing medical errors. CDSS uses electronic health records, medical knowledge databases, and advanced algorithms to provide evidence-based guidance for clinical decision-making. However, regardless of its positive impact in the healthcare industry CDSS has several challenges.

We will be using a case study to better understand the advantages and disadvantages of CDSS. In this case study Dr. Carter is rounding on her patient and she enters the patients’ symptoms, vitals, and history into the EHR system. CDSS then generates a list of different diagnoses as well as alerts on medication interactions, recommendations for a D-imer test and immediate EKG. Lastly it provides a reminder to review a recent imagine report from a Regional Health Information Exchange ( HIE). Dr. Carter follows the systems recommendation, and the results did confirm one of the diagnosis the CDSS generated. This allowed for Dr. Carter to make an informed evidence-based decision.

It is important to note that Dr. Carter is a strong advocate for technology in healthcare and was excited about CDSS being implemented at her hospital. However, the case study mentions that some clinicians felt the CDSS disrupted their workflow. That it was confusing to navigate the system and lastly that data discrepancies between HIE records and internal EHRs caused delays in other cases. Many clinicians do not like change and therefore might find the implementation of CDSS as disruptive and difficult to navigate. According to (Laka et al., 2023) implementation of CDSS is an adaptive process rather than a purely technical process. Therefore, engaging staff in the change process is important and setting up accessible and real time support services to help troubleshoot issues is important to alleviate the fear of a new system. Moreover, while CDSS alerts can be beneficial in reducing medical errors, they can also contribute to staff burnout. According to Wan et al. (2020), systems should adopt blockchain architectural frameworks and smart contracts to generate more relevant and patient-specific alerts, while minimizing inappropriate ones to help reduce burnout."

Moreover, Clinical Decision Support System (CDSS) enhances personalized care by offering treatment recommendations tailored to a patient’s medical history and needs. This ensures that care is relevant and effective. Additionally, CDSS empowers patients by giving them easy-to-understand information about their health, allowing them to actively engage in their treatment decisions. This allows better collaboration between patients and healthcare providers, leading to better outcomes and a more satisfying healthcare experience.
References
Laka, M., Carter, D., Milazzo, A., & Merlin, T. (2022). Challenges and opportunities in implementing clinical decision support systems (CDSS) at scale: Interviews with Australian policymakers. ScienceDirect. https://www.sciencedirect.com/science/article/pii/S2211883722000600

Wan, P. K., Satybaldy, A., Huang, L., Holtskog, H., & Nowostawski, M. (2020). Reducing alert fatigue by sharing low-level alerts with patients and enhancing collaborative decision making using blockchain technology: Scoping review and proposed framework (MedAlert). National Library of Medicine. https://pmc.ncbi.nlm.nih.gov/articles/PMC7657729/

“Empowering Healthcare with Clinical Decision Support Systems”

Clinical Decision Support Systems have facilitated decision making processes in healthcare by improving the way certain tasks are performed. Over the past few decades, CDSS’s have evolved to become both strategic and tactical tools that offer strategic benefit to healthcare organizations by tactically assisting clinicians during the patient care process (Brown et al., 2019). Since the enactment of the HITECH Act in 2009 and the implementation of EHR systems by healthcare organizations to enhance care quality, clinical decision support systems have been highly regarded as valuable tools for improving the quality and delivery of healthcare. Clinical Decision Support Systems have successfully provided support to healthcare providers, staff, and patients with tailored knowledge and filtered data information. This data is delivered precisely when needed, enhancing the quality and efficiency of care. However, there are limitations on the effectiveness of these systems. One specific limitation faced by users is the integration and data exchange with other systems. The system’s inability to accurately and efficiently exchange data reduces the clinical decision support systems effectiveness which can affect patient care.
Although clinical decision support systems have proven to be a useful tool in healthcare, they have also faced many challenges and barriers. Six areas where barriers are encountered are: data, knowledge, inference, technology, interoperability, and users (Brown et al., 2019). I believe that interoperability and users are the most important because as clinical decision support systems are continuously evolving, they pose a challenge for the users. Users must be able to access and interact across various platforms with minimal effort to optimize the useability of these systems. The human factor plays a significant role in the successful and efficient implementation of clinical decision support systems. An example based on this case study would be the adoption and adaptation of these systems among clinical staff. Clinicians may be resistant to adopting new technologies such as CDSS if they find them complex or time consuming. Focusing on making these systems user friendly may reduce frustration, errors, and improve efficiency. A CDSS should be designed so that it is easy to view, understand, and use. Poor usability-a lack of “user friendliness” may not only diminish user satisfaction but also negatively affect the user’s opinion of other CDSSs and lead to errors (Wu, Davis, and Bell 2012). In addition, minimizing excessive or irrelevant alerts may overwhelm the user resulting alerts being ignored or dismissed. A way to minimize staff push back would be to properly train staff on the use of these systems for more effective utilization. Providing comprehensive training and ongoing support can help users become proficient and comfortable with the CDSS.
References:
Brown, G. D., Pasupathy, K. S., & Patrick, T. B. (2019). Health informatics a systems perspective (2nd ed.). Health Administration Press Association of University Programs in Health Administration (AUPHA).
Wu, H. W., Davis, P. K., & Bell, D. S. (2012). Advancing clinical decision support using lessons from outside of healthcare: an interdisciplinary systematic review. BMC medical informatics and decision making, 12, 90. https://doi.org/10.1186/1472-6947-12-90

“CDSS: Working Hard or Hardly Working”

Clinical Decision Support Systems is a form of technology that allows us to quickly get an abundance of information when we plug in key terms such as age, height, weight and so on. This next step in technology allows us to calculate information in seconds, which saves time and gives accurate statistics that pull information from credible sources. CDSS would improve healthcare outcomes by allowing patients to anticipate health concerns and find adequate treatments to battle said concerns in good timing. According to the study “ Enhancing Clinical Decision-Making with Knowledge-Based Systems”, with the integration of Electronic Health Records, CDSS is able to input information from patients such as symptoms and past medical history to generate possible diagnoses as well as treatment plans,recommendations and reminders.
Clinical Decision Support Systems allow for patients to be able to avoid health issues to worsen by preventative maintenance as well as planning. Healthcare has always been complicated to deal with but having access to CDSS and implementing EHR to it helps individuals who have access to internet or health care to be able to view their possible diagnoses, set alert reminders regarding medication as well health recommendations. Though CDSS has a lot of positive qualities, some cons still remain. According to the study, many clinicians fear for job security with the integration of CDSS to the medical field. Clinicians claim their workflow is disrupted and this can be due to the stress these individuals feel knowing that a machine can effectively do their job quicker so they don’t feel secure in the workspace as well difficulties some face when attempting to navigate the systems interface.
Some potential challenges Clinicians can face would be feeling as if they are no longer included in the decision making process and feeling worn out from all the system’s notifications. One method to make sure Clinicians still hold the crucial part in decision making process when working with CDSS would be to change the formatting of the systems interface to show possible diagnoses and treatment options as recommendations instead of overriding Clinicians proposals. Another method to prevent Clinicians from feeling worn out from excessive notifications would be to be able to mute and categorize different types of alerts to give the option to not have all of them loudly go off by categorizing levels of severity.
CDSS is only able to operate if any input is put in to be able to narrow information to give more accurate answers. By collecting patient input, CDSS helps enhance personalized care by tailoring diagnosis, treatment methods and medical alerts to each different case to better serve all health care patients. All suggestions CDSS generates are based off credible sources, “By layering evidence-based best practices on top of unique patient information found in EHRs, CDS tools can present the clinician with knowledge that is tailored to the patient to inform more personalized care decisions to engage patients and caregivers throughout their care journey” ( Mujumdar, 2022).
References:
Mujumdar, V., & Jeffcoat, H. (2022, September 1). How Clinical Decision Support Tools Can Be Used to Support Modern Care Delivery. ACS. https://www.facs.org/for-medical-professionals/news-publications/news-and-articles/bulletin/2022/september-2022-volume-107-issue-9/how-clinical-decision-support-tools-can-be-used-to-support-modern-care-delivery/

Sutton, R., Pincock, D., Baumgart, D., Sadowski, D., Fedorak, R., & Kroeker, K. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine, 3(1), 1–10. https://doi.org/10.1038/s41746-020-0221-y

The Role of CDSS in Clinical Decision-Making
CDSS offers evidence-based suggestions that are customized to meet the needs of each patient, it can be a very useful tool in the decision-making process. The system played a crucial role in Dr. Carter's diagnosis of Mr. John Smith, a 63-year-old man who had complained of shortness of breath and chest pain. In addition to making crucial recommendations like scheduling an EKG and a D-dimer test, the system promptly produced differential diagnoses, such as acute coronary syndrome, pulmonary embolism, and pneumonia.

The decision-making process was further aided by the CDSS's identification of a possible pharmaceutical interaction. These features guarantee that decisions are based on the most recent research because they are supported by comprehensive databases and clinical recommendations. CDSS can lower the likelihood of errors by providing a thorough overview of diagnostic and treatment alternatives, especially in cases that are complex or time-sensitive. Even while CDSS can greatly enhance therapeutic results, it's important to be aware of its limitations. One significant drawback is that CDSS cannot take the place of medical professionals' knowledge. Although the system provides recommendations and serves as a support tool, it should never take the place of seasoned professionals' clinical judgment. In order to ensure that decisions are customized to each patient's particular circumstances, clinicians must continue to actively participate in the interpretation and use of the system's recommendations.
Challenges and Solutions
The Metro Health Hospital's CDSS installation brought to light a number of issues that clinicians experience and that must be resolved in order to maximize the utilization of the system.
1. Workflow Disruption: A few physicians believed that the CDSS interfered with their routine. Despite being intended to offer prompt advise, the deluge of notifications led to alert fatigue, which reduced the system's efficacy. Too many or too frequent alerts might overwhelm medical professionals, causing them to pay less attention to important details.

Solution: Hospitals should put in place a system that ranks notifications according to their importance and urgency in order to reduce alert fatigue. In order to prevent critical warnings from becoming lost in a sea of less urgent notifications, customization options would enable physicians to customize the kinds of notifications they get. The utility of the system can be further improved by adding a feedback loop that enables medical professionals to report recurrent or unnecessary alerts.

2. System Interface Confusion: Healthcare personnel, including nurses, expressed trouble utilizing the system interface. In order to guarantee that healthcare professionals can use the CDSS efficiently, the user experience is essential. Care delays may result from staff members finding it difficult to enter required data or get crucial advice if the system is not clear or easy to use.
Solution: To acquaint all healthcare personnel with the CDSS interface, a thorough training program ought to be established. To make the system more user-friendly, clinicians' opinions should be included into a revised design. Efficiency would increase and confusion would be decreased if the interface were made simpler and more in line with clinicians' natural processes.

3. Inconsistencies in Data Between HIE Records and EHR: There have occasionally been inconsistencies in the integration of data from various sources, including internal EHR systems and the Health Information Exchange (HIE), which have delayed diagnosis and treatment. Clinicians may receive contradicting information, which could affect the accuracy of their conclusions and make clinical decision-making more difficult.

Solution: Reducing data disparities requires better interoperability between the EHR and HIE platforms. Hospitals can guarantee that data is constantly correct and current by standardizing data formats and improving system-to-system connectivity. To find and fix discrepancies before they have an impact on patient care, routine audits and data reconciliation procedures should be put in place.

Patient-Centered Approach

To guarantee that CDSS contributes to individualized care, a patient-centered approach is essential. Incorporating the patient's own preferences and values into the decision-making process is crucial, even when the system provides clinical recommendations based on patient data. Dr. Carter confirmed the diagnosis and started the proper treatment for Mr. Smith by using the system's recommendations. However, patient-specific aspects like Mr. Smith's comprehension of the ailment or his personal preferences regarding treatment alternatives were not taken into consideration by the algorithm.
CDSS might be created to encourage physicians to involve patients in the decision-making process in order to more effectively integrate a patient-

“Intelligent Insights Beyond the Stethoscope”

The Role of CDSS in Clinical Decision-Making:
Advantages: CDSS integrates technology and healthcare to highlight a patient’s health issues and suggest treatment options to improve their care. It has the power to provide more timely, efficient diagnoses based on a patient’s medical data, becoming all the more essential to the decision-making process (Brown et al, 2019). It decreases the need for excessive tests, often pinpointing the root cause of a patient’s health condition. In this way, human errors also reduce, and patient safety improves. By incorporating CDSS, treatments become more personalized as recommendations are based on the individual patient’s medical data, genetics, etc. as demonstrated in establishing Mr. Smith’s case of diabetes and hypertension.
Limitations: If healthcare providers do not have access to the most up to date technology, healthcare information, or proper training to use various technologies, then patient care can be hindered. Data needs to be complete, and accurate or it may lead to incorrect recommendations that disrupt the clinical decision making process. Clinicians should also not completely rely on CDSS to fulfill their jobs. Instead, it should be used as support to produce their own work as the case study meeting concluded. Clinicians need to still use their judgment to offer best diagnoses for patient care.

Challenges and Solutions:
Challenge: Dr. Carter noted that with excessive alerts, clinicians believed there may be a disruption in workflow. With too much information, it is possible they do not know what is relevant to patient care and find it cumbersome trying to sift through it all.
Solution: A solution could be integrating a tool that ranks the alerts by most useful or most practical and then simply providing those. For example, a doctor may be given five alerts for a patient’s treatment plan, but truthfully, they would only find one useful. In this way, the doctor can identify which one that is, and then, only that alert would be given.
Challenge: With any new system, learning to navigate it is tricky, especially for healthcare staff who may be unfamiliar with technology. In fact, it may even cause stress, confusion, and more unwanted medical errors.
Solution: In this case, training programs could explain to Dr. Carter’s staff about how to use the interface so they feel comfortable trusting it (Brown et al, 2019). She may also ask her staff if they have any suggestions about improving the system to tailor it to their needs. Just as important as it is to determine what worked well, users must also figure out what design factors led to dissatisfaction so they can avoid them (Mastrian & McGonigle, 2021).

Patient-Centered Approach:
When clinicians spend less time on burdensome tasks, they can focus more of their efforts on patient interaction. This can help patients feel more involved in the decision making process and more trusting of their healthcare providers in providing thoughtful care (Brown et al, 2019). In order to fully adopt a patient-centered approach, knowing Mr. Smith’s genetic history and current lifestyle would enable improvement of his daily well-being. For example, CDSS can use real-time monitoring of Mr. Smith’s health by suggesting the use of wearables like smartwatches. That way, his heart rate and other vitals could be constantly recorded. Using this data, Dr. Carter can refine his short-term goals to better fit his overall health outcomes.

References:
Brown, G. D., Pasupathy, K. S., & Patrick, T. B. (2019). Health informatics : a systems perspective (Second edition.). Health Administration Press.
Kathleen Mastrian, & Dee McGonigle. (2021). Informatics for Health Professionals: Vol. Second edition. Jones & Bartlett Learning.

Knowledge-Based Decision Making CDSS: A Provider's Friend or Foe?

The Role of CDSS in Clinical Decision-Making
Clinical Decision Support Systems (CDSS) play an essential role in improving healthcare outcomes. This technology assists clinicians in making accurate and timely decisions by analyzing patient data and offering evidence-based recommendations. CDSS enhances diagnostic precision, reduces medication errors, and ensures best practices are conducted when treating a patient. In Dr. Carter’s case, CDSS helped confirm a pulmonary embolism diagnosis and flagged a potentially dangerous medication interaction, preventing harm. However, over-reliance on automated suggestions may lead to reduced clinician autonomy, and excessive alerts can contribute to fatigue, causing critical warnings to be ignored.
Challenges and Solutions
CDSS implementation can also present several challenges. One major issue is alert fatigue, where clinicians become overwhelmed by frequent notifications, leading to desensitization. To address this, hospitals can implement tiered alert systems, prioritizing urgent alerts while minimizing non-critical interruptions. Another challenge is workflow disruption, as some clinicians find CDSS integration increases interruptions. Customizing the system to align with existing clinical workflows and improving the user interface can enhance its usability. Concerns over clinician autonomy is what has been talked about with CDSS. To counter this, CDSS should be designed as a supportive tool that enhances decision-making without overriding professional judgment.
Patient-Centered Approach
CDSS should incorporate patient input and promote personalized care. One approach is integrating patient portals, allowing individuals to input symptoms, medication adherence, and health preferences. This data can refine CDSS recommendations to align with patient-specific needs. Shared decision-making tools enable clinicians to discuss options with patients, ensuring treatments align with their values and lifestyle. CDSS enhances patient outcomes by optimizing clinical workflows, lowering mortality rates, and supporting evidence-based decision-making. They also contribute to clinician satisfaction by offering real-time insights and alleviating cognitive workload (Chen, et., al, 2023). Furthermore, adaptive learning algorithms can tailor recommendations based on patient responses, continuously improving the system’s accuracy and relevance. Actively involving patients in the decision-making process, CDSS can enhance personalized care while fostering trust between providers and patients.

Chen, Z., Liang, N., Zhang, H., Li, H., Yang, Y., Zong, X., Chen, Y., Wang, Y., & Shi, N. (2023). Harnessing the power of clinical decision support systems: challenges and opportunities. Open heart, 10(2), e002432. https://doi.org/10.1136/openhrt-2023-002432

Enhancing clinicians lives: CDSS

Clinical decision support systems have "been shown to improve physician performance and patient care, as well as to reduce healthcare cost"(Brown) especially if built right. CDSS allows for recommendations to users automatically anticipating next steps to ease processes and efficiency. The biggest success rates of CDSS have been integrations into electronic health care records allowing for quick responses and use. CDSS are knowledge based presenting information to assist but allowing the clinician or final user to make the decision. CDSS does not come without its challenges and does not replace clinicians. 

In the case study enhancing clinical decision making with knowledge based systems Dr.Carter deals with both the benefits and challenges of CDSS. In Dr.Carter's patient scenario we see how CDSS assists the provider with recommendations for tests, exams, and diagnoses. However, these recommendations come in flags and warnings that lead to a disrupted workflow and with constant recommendations lead to fatigue. A survey for the US veterans department found that PCPs "averaged 63 alerts per day and about 70% reported more alerts that they considered manageable"(Brown). Alerts are important but for Dr.Carters case it would be important to reduce alerts to only higher significance items. Lastly introducing CDSS systems requires training and time to adopt regularly scheduled meetings with training on how to use CDSS properly are key to effect adoption of the system. 

The patient centered approach model has been highlighted as essential to the future of healthcare. CDSS can incorporate patients personal life style such as "each individual’s circumstances and preferences"(Agency for Healthcare Research and Quality). We see this now in smaller settings like diabetes management using wearable technology that allows patients and doctors to follow their glucose levels. Currently CDSS is highly used in specialized fields only, such as medication management like Medsocket that assists in recommendations for the best medication to use for clinicians. 

CDSS is huge and can lead to improving patient care and reducing errors. However, it doesn't come without its issues such as usability, alert fatigue, and adoption. Combating these issues and working with the end users clinicians. Allows for feedback to be heard, changes to be made, and support staff rather than intrude. Finally, new onboarding and continuous training with updates are key for a successful CDSS.  

Agency for Healthcare Research and Quality. (2024, May 1). Clinical Decision Support. CDSiC. https://cdsic.ahrq.gov/cdsic/patient-centered-clinical-cds-infographic#:~:text=Patient%2DCentered%20Clinical%20Decision%20Support%20(PC%20CDS)%20includes%20digital,between%20patients%20and%20their%20clinicians.

Brown, G. D., Pasupathy, K. S., & Patrick, T. B. (Eds.). (2019). Health informatics: A systems

CDSS: Revolutionizing Healthcare

CDSS can certainly improve healthcare outcomes that provide clinicians with timely, evidence-based insights. In the case study, CDSS assisted Dr. Carter in identifying differential diagnoses for patient Mr. John Smith, diagnostic tests such as D-dimer and an EKG, as well as highlighting potential adverse drug interactions. These recommendations helped Dr. Carter in diagnosing the patient with a pulmonary embolism timely. PEs are notably life-threatening when not treated timely, thus without the support of the CDSS a serious diagnosis may been delayed which can lead to poorer patient outcomes. Decisions are normally made during direct patient contact with a patient's current state but with CDSS, a patient’s history and data prior to a visit would be deemed essential in determining a solution. (Wasylewicz et al, 2018)

However, CDSS does have its limitations. For example, staff at Metro Health Hospital have stated they are facing alert fatigue from the new system. It has been expressed that this also creates disruptions to normal workflows, potentially causing errors to patient care and delivery. There are also concerns with CDSS could override clínical autonomy, which undermines the judgement of physicians with automated suggestions.

Challenges and Solutions:

Wasylewicz et al suggests that CDSS is an evolving technology that needs a thoughtful design, implementation and critical evaluation.

This case study has highlighted several hurdles such as the disruption of clinicians’ workflow and confusion of the system’s interface among the nurses. To address this, hospitals should focus on thorough training for all staff members, especially those that will be utilizing the enhanced system. A training that focuses on the support staffs’ issues and concerns would benefit them as well as alleviate confusion. This would increase utility and satisfaction rates of this system.

To combat alert fatigue, hospitals should be able to customize the frequency of alerts that clinicians receive. The system should implement customization for each individual clinician based on their needs and preferences. For example, a physician may want to remove less critical notifications and keep the most severe notifications. This personalization could alleviate the mental burnout of healthcare providers.

Data discrepancies between the internal system and Health Information Exchange can pose challenges for clinicians such as a delay in timely decision-making. To address this discrepancy, hospitals should prioritize standardizing interoperability in data sharing to ensure data is up-to-date and accessible by all that need access. A seamless integration between health record systems is essential in delivering quality care.

Patient Centered Approach:

Incorporating a patient-centered approach into CDSS is vital to ensuring personalized care. In the case study, it was not clearly implied that patient input was involved in CDSS usage but it would benefit the hospital and all healthcare in general. Involving patients in the decision-making process can prove beneficial to a patient’s care. For example, CDSS can prompt clinicians in integrating a patient’s preference in their treatment plan such as “no blood donations due to a religious preference”. When patient’s feel included in their treatment plan, they feel more inclined to remain responsible in their part to remaining healthy individuals. Patients when utilizing CDSS to personalize their care fosters a collaborative relationship between patient and clinician, leading to a more personalized and optimal care.

Overall, CSS can enhance patient care through evidence based recommendations. With its limitations and challenges, it is up to healthcare providers to be leaders in refining CDSS systems to their patients needs. With thorough implementation, CDSS can revolutionize healthcare that respects healthcare professional expertise and individuality of each patient.

Wasylewicz ATM, Scheepers-Hoeks AMJW. Clinical Decision Support Systems. 2018 Dec 22. In: Kubben P, Dumontier M, Dekker A, editors. Fundamentals of Clinical Data Science [Internet]. Cham (CH): Springer; 2019. Chapter 11. Available from: https://www.ncbi.nlm.nih.gov/books/NBK543516/doi: 10.1007/978-3-319-99713-1_11

“CDSS: A Glimpse into the Future?”

Clinical Decision Support Systems (CDSS) was implemented at Dr. Carter’s hospital with the intention of “offering evidence-based recommendations for diagnostics, treatments, and preventative care.” As was discussed in the background of the case the alert fatigue was a primary concern brought up to Dr. Carter by her colleagues along with the lack of autonomy in the face of this new CDSS. This is common in the implementation of new systems. Colleagues are resistant to change and fear for the adjustments in their workflows and the ramifications around any new change to their job or job function. As a provider, many are concerned over patients and the impact that any change would have on patient care. While this is a valid concern and something that should be planned for and addressed, the CDSS that was implemented appears to have been vetted effectively in this area.
In Dr. Carters’ scenario it was clear to see the benefits of a CDSS system. Dr. Carter was able to input the patient’s symptoms, vitals and medical history into the EHR. Due to the effective interoperability between the CDSS and the EHR, the CDSS was then able to quickly identify different potential diagnoses along with a recommendation of future workups for Dr. Carter to complete. Dr. Carter was able to order the additional workups and lab work and also identify an alert from the CDSS that the patient may have medications that interacted adversely. This is a key feature and something that could have been missed if not for the CDSS and highlights an area in which CDSS can excel in improving patient safety. Moreover, Dr. Carter completed the workups and then did eventually confirm one of the diagnoses that were initially suspected based on the patients symptoms, vitals and medical history. Here, due to the vast resources and dataset that the CDSS was able to pull from, the best medical treatment was identified and then completed in a prompt time. It also alerts for other imaging done at a different facility; due to the linking between the EHR this gives the provider but also the CDSS the full scope of the patient’s current and past medical history. This could be expanded upon in the future to include a component for patient’s to view and provide questions for patients to ask their doctor regarding a specific diagnosis or recommended workup. This gives patients more information regarding their health and also encourages more patient to doctor and vice versa communication.
Ultimately, this situation addresses physician’s main concerns regarding the CDSS. They still have autonomy to complete the treatments that they feel would be best for the patient. However, it provides them with recommended courses of action based on historical data. Assuming the validity of that dataset, the physician is likely to have treatment that aligns with the suggestions from the CDSS. The only concern that still exists is the alert fatigue. This is something to explore that potentially only has alerts for critical errors that would occur or emergent patients and circumstances. Thus, any alert would have a high urgency and thus again act in the interest of patient safety and reduce the number of alerts physicians would otherwise receive from the CDSS.

References

Brown, G. D., Pasupathy, K. S., & Patrick, T. B. (2019). Health informatics : a systems perspective (Second edition.). Health Administration Press.

Kathleen Mastrian, & Dee McGonigle. (2021). Informatics for Health Professionals: Vol. Second edition. Jones & Bartlett Learning.