Clinical decision support system

A clinical decision support system (CDSS) is a health information technology that provides clinicians, staff, patients, and other individuals with knowledge and person-specific information to help health and health care. CDSS encompasses a variety of tools to enhance decision-making in the clinical workflow. These tools include computerized alerts and reminders to care providers and patients, clinical guidelines, condition-specific order sets, focused patient data reports and summaries, documentation templates, diagnostic support, and contextually relevant reference information, among other tools. CDSSs constitute a major topic in artificial intelligence in medicine.

Characteristics

A clinical decision support system is an active knowledge system that uses variables of patient data to produce advice regarding health care. This implies that a CDSS is simply a decision support system focused on using knowledge management.

Purpose

The main purpose of modern CDSS is to assist clinicians at the point of care.^[1] This means that clinicians interact with a CDSS to help to analyze and reach a diagnosis based on patient data for different diseases.

In the early days, CDSSs were conceived to make decisions for the clinician literally. The clinician would input the information and wait for the CDSS to output the "right" choice, and the clinician would simply act on that output. However, the modern methodology of using CDSSs to assist means that the clinician interacts with the CDSS, utilizing both their knowledge and the CDSS's, better to analyse the patient's data than either human or CDSS could make on their own. Typically, a CDSS makes suggestions for the clinician to review, and the clinician is expected to pick out useful information from the presented results and discount erroneous CDSS suggestions.^[2]

The two main types of CDSS are knowledge-based and non-knowledge-based:^[1]

An example of how a clinician might use a clinical decision support system is a diagnosis decision support system (DDSS). DDSS requests some of the patients' data and, in response, proposes a set of appropriate diagnoses. The physician then takes the output of the DDSS and determines which diagnoses might be relevant and which are not,^[1] and, if necessary, orders further tests to narrow down the diagnosis.

Another example of a CDSS would be a case-based reasoning (CBR) system.^[3] A CBR system might use previous case data to help determine the appropriate amount of beams and the optimal beam angles for use in radiotherapy for brain cancer patients; medical physicists and oncologists would then review the recommended treatment plan to determine its viability.^[4]

Another important classification of a CDSS is based on the timing of its use. Physicians use these systems at the point of care to help them as they are dealing with a patient, with the timing of use being either pre-diagnosis, during diagnosis, or post-diagnosis.^{[citation needed]} Pre-diagnosis CDSS systems help the physician prepare the diagnoses. CDSSs help review and filter the physician's preliminary diagnostic choices to improve outcomes. Post-diagnosis CDSS systems are used to mine data to derive connections between patients and their past medical history and clinical research to predict future events.^[1] As of 2012, it has been claimed that decision support will begin to replace clinicians in common tasks in the future.^[5]

Another approach, used by the National Health Service in England, is to use a DDSS to triage medical conditions out of hours by suggesting a suitable next step to the patient (e.g. call an ambulance, or see a general practitioner on the next working day). The suggestion, which may be disregarded by either the patient or the phone operative if common sense or caution suggests otherwise, is based on the known information and an implicit conclusion about what the worst-case diagnosis is likely to be; it is not always revealed to the patient because it might well be incorrect and is not based on a medically-trained person's opinion - it is only used for initial triage purposes.^{[citation needed]}

Knowledge-based

Most CDSSs consist of three parts: the knowledge base, an inference engine, and a mechanism to communicate.^[6] The knowledge base contains the rules and associations of compiled data which most often take the form of IF-THEN rules. If this was a system for determining drug interactions, then a rule might be that IF drug X is taken AND drug Y is taken THEN alert the user. Using another interface, an advanced user could edit the knowledge base to keep it up to date with new drugs. The inference engine combines the rules from the knowledge base with the patient's data. The communication mechanism allows the system to show the results to the user as well as have input into the system.^[2][1]

An expression language such as GELLO^{[clarification needed]} or CQL (Clinical Quality Language) is needed for expressing knowledge artefacts in a computable manner. For example: if a patient has diabetes mellitus, and if the last haemoglobin A1c test result was less than 7%, recommend re-testing if it has been over six months, but if the last test result was greater than or equal to 7%, then recommend re-testing if it has been over three months.

The current focus of the HL7 CDS WG is to build on the Clinical Quality Language (CQL).^[7] The U.S. Centers for Medicare & Medicaid Services (CMS) has announced that it plans to use CQL for the specification of Electronic Clinical Quality Measures (eCQMs).^[8]

Non-knowledge-based

CDSSs which do not use a knowledge base use a form of artificial intelligence called machine learning,^[9] which allow computers to learn from past experiences and/or find patterns in clinical data. This eliminates the need for writing rules and expert input. However, since systems based on machine learning cannot explain the reasons for their conclusions, most clinicians do not use them directly for diagnoses, reliability and accountability reasons.^[2][1] Nevertheless, they can be useful as post-diagnostic systems, for suggesting patterns for clinicians to look into in more depth.

As of 2012, three types of non-knowledge-based systems are support-vector machines, artificial neural networks and genetic algorithms.^[10]

1. Artificial neural networks use nodes and weighted connections between them to analyse the patterns found in patient data to derive associations between symptoms and a diagnosis.
2. Genetic algorithms are based on simplified evolutionary processes using directed selection to achieve optimal CDSS results. The selection algorithms evaluate components of random sets of solutions to a problem. The solutions that come out on top are then recombined and mutated and run through the process again. This happens over and over until the proper solution is discovered. They are functionally similar to neural networks in that they are also "black boxes" that attempt to derive knowledge from patient data.
3. Non-knowledge-based networks often focus on a narrow list of symptoms, such as symptoms for a single disease, as opposed to the knowledge-based approach, which covers the diagnosis of many diseases.^[2][1]

An example of a non-knowledge-based CDSS is a web server developed using a support vector machine for the prediction of gestational diabetes in Ireland. ^[11]

Regulations

History, United States

The IOM had published a report in 1999, To Err is Human, which focused on the patient safety crisis in the United States, pointing to the incredibly high number of deaths. This statistic attracted great attention to the quality of patient care.^{[citation needed]} The Institute of Medicine (IOM) promoted the usage of health information technology, including clinical decision support systems, to advance the quality of patient care.^[12]

With the enactment of the American Recovery and Reinvestment Act of 2009 (ARRA), there was a push for widespread adoption of health information technology through the Health Information Technology for Economic and Clinical Health Act (HITECH). Through these initiatives, more hospitals and clinics were integrating electronic medical records (EMRs) and computerized physician order entry (CPOE) within their health information processing and storage. Despite the absence of laws, the CDSS vendors would almost certainly be viewed as having a legal duty of care to both the patients who may adversely be affected due to CDSS usage and the clinicians who may use the technology for patient care.^{[citation needed][clarification needed]} However, duties of care legal regulations are not explicitly defined yet. With the enactment of the HITECH Act included in the ARRA, encouraging the adoption of health IT, more detailed case laws for CDSS and EMRs were still^[when?] being defined by the Office of National Coordinator for Health Information Technology (ONC) and approved by Department of Health and Human Services (HHS). A definition of "Meaningful use" has yet to be published.^{[clarification needed]}

Effectiveness

The evidence of the effectiveness of CDSS is mixed. There are certain diseases which benefit more from CDSS than other disease entities. A 2018 systematic review identified six medical conditions in which CDSS improved patient outcomes in hospital settings, including blood glucose management, blood transfusion management, physiologic deterioration prevention, pressure ulcer prevention, acute kidney injury prevention, and venous thromboembolism prophylaxis.^[13] A 2014 systematic review did not find a benefit in terms of risk of death when the CDSS was combined with the electronic health record.^[14] There may be some benefits, however, in terms of other outcomes.^[14] A 2005 systematic review had concluded that CDSSs improved practitioner performance in 64% of the studies and patient outcomes in 13% of the studies. CDSSs features associated with improved practitioner performance included automatic electronic prompts rather than requiring user activation of the system.^[15]

A 2005 systematic review found "Decision support systems significantly improved clinical practice in 68% of trials."' The CDSS features associated with success included integration into the clinical workflow rather than as a separate log-in or screen, electronic rather than paper-based templates, providing decision support at the time and location of care rather than prior, and providing care recommendations.^[16]

However, later systematic reviews were less optimistic about the effects of CDS, with one from 2011 stating "There is a large gap between the postulated and empirically demonstrated benefits of [CDSS and other] eHealth technologies ... their cost-effectiveness has yet to be demonstrated".^[17]

A five-year evaluation of the effectiveness of a CDSS in implementing rational treatment of bacterial infections for antimicrobial stewardship was published in 2014; according to the authors, it was the first long-term study of a CDSS.^[18]

Challenges to adoption

Clinical challenges

Much effort has been put forth by many medical institutions and software companies to produce viable CDSSs to support all aspects of clinical tasks. However, with the complexity of clinical workflows and the demands on staff time high, care must be taken by the institution deploying the support system to ensure that the system becomes an integral part of the clinical workflow. Some CDSSs have met with varying amounts of success, while others have suffered from common problems preventing or reducing successful adoption and acceptance.

Two sectors of the healthcare domain in which CDSSs have had a large impact are the pharmacy and billing sectors. Commonly used pharmacy and prescription-ordering systems now perform batch-based checking orders for negative drug interactions and report warnings to the ordering professional. Another sector of success for CDSS is in billing and claims filing. Since many hospitals rely on Medicare reimbursements to stay in operation, systems have been created to help examine both a proposed treatment plan and the current rules of Medicare to suggest a plan that attempts to address both the care of the patient and the financial needs of the institution.^{[citation needed]}

Other CDSSs that are aimed at diagnostic tasks have found success, but are often very limited in deployment and scope. The Leeds Abdominal Pain System went operational in 1971 for the University of Leeds hospital. It was reported to have produced a correct diagnosis in 91.8% of cases, compared to the clinicians' success rate of 79.6%.^{[citation needed]}

Despite the wide range of efforts by institutions to produce and use these systems, widespread adoption and acceptance have still not yet been achieved for most offerings. One large roadblock to acceptance has historically been workflow integration. A tendency to focus only on the functional decision-making core of the CDSS existed, causing a deficiency in planning how the clinician will use the product in situ. CDSSs were stand-alone applications, requiring the clinician to cease working on their current system, switch to the CDSS, input the necessary data (even if it had already been inputted into another system), and examine the results produced. The additional steps break the flow from the clinician's perspective and cost precious time.^{[citation needed][19]}

Technical challenges and barriers to implementation

Clinical decision support systems face steep technical challenges in a number of areas. Biological systems are profoundly complicated, and a clinical decision may utilise an enormous range of potentially relevant data. For example, an electronic evidence-based medicine system may potentially consider a patient's symptoms, medical history, family history and genetics, as well as historical and geographical trends of disease occurrence, and published clinical data on therapeutic effectiveness when recommending a patient's course of treatment.

Clinically, a large deterrent to CDSS acceptance is workflow integration.

While it has been shown that clinicians require explanations of Machine Learning-Based CDSS, in order to able to understand and trust their suggestions,^[20] there is an overall distinct lack of application of explainable Artificial Intelligence in the context of CDSS,^[21] thus adding another barrier to the adoption of these systems.

Another source of contention with many medical support systems is that they produce a massive number of alerts. When systems produce a high volume of warnings (especially those that do not require escalation), besides the annoyance, clinicians may pay less attention to warnings, causing potentially critical alerts to be missed. This phenomenon is called alert fatigue. ^[22]

Maintenance

One of the core challenges facing CDSS is difficulty in incorporating the extensive quantity of clinical research being published on an ongoing basis. In a given year, tens of thousands of clinical trials are published.^[23] Currently, each one of these studies must be manually read, evaluated for scientific legitimacy, and incorporated into the CDSS in an accurate way. In 2004, it was stated that the process of gathering clinical data and medical knowledge and putting them into a form that computers can manipulate to assist in clinical decision-support is "still in its infancy".^[24]

Nevertheless, it is more feasible for a business to do this centrally, even if incompletely, than for each doctor to try to keep up with all the research being published.^{[citation needed]}

In addition to being laborious, integration of new data can sometimes be difficult to quantify or incorporate into the existing decision support schema, particularly in instances where different clinical papers may appear conflicting. Properly resolving these sorts of discrepancies is often the subject of clinical papers itself (see meta-analysis), which often take months to complete.^{[citation needed]}

Evaluation

In order for a CDSS to offer value, it must demonstrably improve clinical workflow or outcome. Evaluation of CDSS quantifies its value to improve a system's quality and measure its effectiveness. Because different CDSSs serve different purposes, no generic metric applies to all such systems; however, attributes such as consistency (with and with experts) often apply across a wide spectrum of systems.^[25]

The evaluation benchmark for a CDSS depends on the system's goal: for example, a diagnostic decision support system may be rated based upon the consistency and accuracy of its classification of disease (as compared to physicians or other decision support systems). An evidence-based medicine system might be rated based upon a high incidence of patient improvement or higher financial reimbursement for care providers.^{[citation needed]}

Combining with electronic health records

Implementing EHRs was an inevitable challenge. This challenge is because it is a relatively uncharted area, and there are many issues and complications during the implementation phase of an EHR. This can be seen in the numerous studies that have been undertaken.^{[citation needed]} However, challenges in implementing electronic health records (EHRs) have received some attention. Still, less is known about transitioning from legacy EHRs to newer systems.^[26]

EHRs are a way to capture and utilise real-time data to provide high-quality patient care, ensuring efficiency and effective use of time and resources. Incorporating EHR and CDSS together into the process of medicine has the potential to change the way medicine has been taught and practiced.^[27] It has been said that "the highest level of EHR is a CDSS".^[28]

Since "clinical decision support systems (CDSS) are computer systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made",^[27] it is clear that it would be beneficial to have a fully integrated CDSS and EHR.

Even though the benefits can be seen, fully implementing a CDSS integrated with an EHR has historically required significant planning by the healthcare facility/organisation for the CDSS to be successful and effective. The success and effectiveness can be measured by the increased patient care being delivered and reduced adverse events occurring. In addition, there would be a saving of time and resources and benefits in terms of autonomy and financial benefits to the healthcare facility/organisation.^[29]

Benefits

A successful CDSS/EHR integration will allow the provision of best practice, high-quality care to the patient, which is the ultimate goal of healthcare. Three areas that can be addressed with the implementation of CDSS and Electronic Health Records (EHRs), are:

1. Medication prescription errors
2. Adverse drug events
3. Other medical errors

CDSSs will be most beneficial in the future when healthcare facilities are "100% electronic" in terms of real-time patient information, thus simplifying the number of modifications that have to occur to ensure that all the systems are up to date with each other.

The measurable benefits of clinical decision support systems on physician performance and patient outcomes remain the subject of ongoing research.

Barriers

Implementing electronic health records (EHR) in healthcare settings incurs challenges; none more important than maintaining efficiency and safety during rollout,^[30] but in order for the implementation process to be effective, an understanding of the EHR users' perspectives is key to the success of EHR implementation projects.^[31] In addition to this, adoption needs to be actively fostered through a bottom-up, clinical-needs-first approach.^[32] The same can be said for CDSS.

As of 2007, the main areas of concern with moving into a fully integrated EHR/CDSS system have been:^[33]

1. Privacy
2. Confidentiality
3. User-friendliness
4. Document accuracy and completeness
5. Integration
6. Uniformity
7. Acceptance
8. Alert desensitisation

as well as the key aspects of data entry that need to be addressed when implementing a CDSS to avoid potential adverse events from occurring. These aspects include whether:

• correct data is being used
• all the data has been entered into the system
• current best practice is being followed
• the data is evidence-based^{[clarification needed]}

A service oriented architecture has been proposed as a technical means to address some of these barriers.^[34]

Status in Australia

As of July 2015, the planned transition to EHRs in Australia is facing difficulties. Most healthcare facilities are still running completely paper-based systems; some are in a transition phase of scanned EHRs or moving towards such a transition phase.

Victoria has attempted to implement EHR across the state with its HealthSMART program, but it has cancelled the project due to unexpectedly high costs.^[35]

South Australia (SA) however is slightly more successful than Victoria in the implementation of an EHR. This may be because all public healthcare organisations in SA are centrally run.

SA is in the process of implementing "Enterprise patient administration system (EPAS)". This system is the foundation for all public hospitals and health care sites for an EHR within SA, and it was expected that by the end of 2014, all facilities in SA will be connected to it. This would allow for successful integration of CDSS into SA and increase the benefits of the EHR.^[36] By July 2015 it was reported that only 3 out of 75 health care facilities implemented EPAS.^[37]

With the largest health system in the country and a federated rather than a centrally administered model, New South Wales is making consistent progress towards statewide implementation of EHRs. The current iteration of the state's technology, eMR2, includes CDSS features such as a sepsis pathway for identifying at-risk patients based upon data input to the electronic record. As of June 2016, 93 of 194 sites in-scope for the initial roll-out had implemented eMR2.^[38]

Status in Finland

The EBMEDS Clinical Decision Support service provided by Duodecim Medical Publications Ltd is used by more than 60% of Finnish public health care doctors.^[39]

Status in India

There have been many recent initiatives in India to promote digital health. New Platforms are emerging in India like Eka.care, Clinisio, Raxa etc, providing EHR integrated clinical decision support.^[40]

Research

Prescription errors

A study in the UK tested the Salford Medication Safety Dashboard (SMASH), a web-based CDSS application to help GPs and pharmacists find people in their electronic health records who might face safety hazards due to prescription errors. The dashboard was successfully used in identifying and helping patients with already registered unsafe prescriptions and later it helped monitoring new cases as they appeared.^[41][42]

See also

• Gello Expression Language
• International Health Terminology Standards Development Organisation
• Medical algorithm
• Medical informatics
• Personal Health Information Protection Act (a law in force in Ontario)
• Treatment decision support (decision support tools for patients)
• Artificial intelligence in healthcare

References

1. Berner, Eta S., ed. Clinical Decision Support Systems. New York, NY: Springer, 2007.
2. "Decision support systems ." 26 July 2005. 17 Feb. 2009 <[1].
3. Begum, Shahina; Ahmed, Mobyen Uddin; Funk, Peter; Xiong, Ning; Folke, Mia (July 2011). "Case-based reasoning systems in the health sciences: a survey of recent trends and developments". IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews. 41 (4): 421–434. doi:10.1109/TSMCC.2010.2071862. S2CID 22441650. Archived from the original on 10 February 2023. Retrieved 6 December 2019.
4. Khussainova, Gulmira; Petrovic, Sanja; Jagannathan, Rupa (2015). "Retrieval with clustering in a case-based reasoning system for radiotherapy treatment planning". Journal of Physics: Conference Series. 616 (1): 012013. Bibcode:2015JPhCS.616a2013K. doi:10.1088/1742-6596/616/1/012013. ISSN 1742-6596.
5. Khosla, Vinod (4 December 2012). "Technology will replace 80% of what doctors do". CNN. Archived from the original on 28 March 2013. Retrieved 25 April 2013.
6. Peyman., Dehghani Soufi, Mahsa. Samad-Soltani, Taha. Shams Vahdati, Samad. Rezaei-Hachesu. Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic. OCLC 1051933713.
7. "HL7 CDS Standards". HL7 CDS Working Group. Retrieved 25 June 2019.
8. CQL - Clinical Quality Language eCQI Resource Center, accessed 15 February 2022
9. Syeda-Mahmood, Tanveer (March 2015). "Plenary talk: The Role of Machine Learning in Clinical Decision Support". SPIE Newsroom. doi:10.1117/2.3201503.29.
10. Wagholikar, Kavishwar; V. Sundararajan; Ashok Deshpande (2012). "Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions". Journal of Medical Systems. 36 (5): 3029–3049. doi:10.1007/s10916-011-9780-4. PMID 21964969. S2CID 14509743.
11. Du, Yuhan; Rafferty, Anthony R.; McAuliffe, Fionnuala M.; Wei, Lan; Mooney, Catherine (21 January 2022). "An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus". Scientific Reports. 12 (1): 1170. Bibcode:2022NatSR..12.1170D. doi:10.1038/s41598-022-05112-2. PMC 8782851. PMID 35064173.
12. Medicine, Institute of (28 February 2001). Crossing the Quality Chasm: A New Health System for the 21st Century. doi:10.17226/10027. ISBN 978-0-309-46561-8. PMID 25057539.
13. Varghese, J; Kleine, M; Gessner, SI; Sandmann, S; Dugas, M (May 2018). "Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review". Journal of the American Medical Informatics Association. 25 (5): 593–602. doi:10.1093/jamia/ocx100. PMC 7646949. PMID 29036406.
14. Moja, L; Kwag, KH; Lytras, T; Bertizzolo, L; Brandt, L; Pecoraro, V; Rigon, G; Vaona, A; Ruggiero, F; Mangia, M; Iorio, A; Kunnamo, I; Bonovas, S (December 2014). "Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis". American Journal of Public Health. 104 (12): e12–22. doi:10.2105/ajph.2014.302164. PMC 4232126. PMID 25322302.
15. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. (2005). "Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review". JAMA. 293 (10): 1223–38. doi:10.1001/jama.293.10.1223. PMID 15755945.
16. Kensaku Kawamoto; Caitlin A Houlihan; E Andrew Balas; David F Lobach. (2005). "Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success". BMJ. 330 (7494): 765. doi:10.1136/bmj.38398.500764.8F. PMC 555881. PMID 15767266.
17. Black, A.D.; Car, J.; Pagliari, C.; Anandan, C.; Cresswell, K.; Bokun, T.; McKinstry, B.; Procter, R.; Majeed, A.; Sheikh, A. (18 January 2011). "The impact of ehealth on the quality and safety of health care: A systematic overview". PLOS Medicine. 8 (1): e1000387. doi:10.1371/journal.pmed.1000387. PMC 3022523. PMID 21267058.
18. Nachtigall, I; Tafelski, S; Deja, M; Halle, E; Grebe, M C; Tamarkin, A; Rothbart, A; Unrig, A; Meyer, E; Musial-Bright, L; Wernecke, K D; Spies, C (22 December 2014). "Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective 'before/after' cohort study". BMJ Open. 4 (12): e005370. doi:10.1136/bmjopen-2014-005370. PMC 4275685. PMID 25534209.
19. National Academy of Medicine (2018). "Optimizing Strategies for Clinical Decision Support" (PDF). Healthit.gov. Archived (PDF) from the original on 23 April 2018. Retrieved 2 February 2021.
20. Tonekaboni, Sana; Joshi, Shalmali; McCradden, Melissa D.; Goldenberg, Anna (28 October 2019). "What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use". Machine Learning for Healthcare Conference. PMLR: 359–380. arXiv:1905.05134.
21. Antoniadi, Anna Markella; Du, Yuhan; Guendouz, Yasmine; Wei, Lan; Mazo, Claudia; Becker, Brett A.; Mooney, Catherine (31 May 2021). "Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review". Applied Sciences. 11 (11): 5088. doi:10.3390/app11115088. ISSN 2076-3417.
22. Khalifa, Mohamed; Zabani, Ibrahim (2016). "Improving Utilization of Clinical Decision Support Systems by Reducing Alert Fatigue: Strategies and Recommendations". Studies in Health Technology and Informatics. 226: 51–54. ISSN 1879-8365. PMID 27350464.
23. Gluud C, Nikolova D (2007). "Likely country of origin in publications on randomised controlled trials and controlled clinical trials during the last 60 years". Trials. 8: 7. doi:10.1186/1745-6215-8-7. PMC 1808475. PMID 17326823.
24. Gardner, Reed M (April 2004). "Computerized Clinical Decision-Support in Respiratory Care". Respiratory Care. 49 (4): 378–388. PMID 15030611.
25. Wagholikar, K; Kathy L. MacLaughlin; Thomas M Kastner; Petra M Casey; Michael Henry; Robert A Greenes; Hongfang Liu; Rajeev Chaudhry (2013). "Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening". Journal of the American Medical Informatics Association. 20 (4): 747–759. doi:10.1136/amiajnl-2013-001613. PMC 3721177. PMID 23564631.
26. Zandieh, Stephanie O.; Kahyun Yoon-Flannery; Gilad J. Kuperman; Daniel J. Langsam; Daniel Hyman; Rainu Kaushal (2008). "Challenges to EHR Implementation in Electronic- Versus Paper-based Office Practices". Journal of Global Information Management. 23 (6): 755–761. doi:10.1007/s11606-008-0573-5. PMC 2517887. PMID 18369679.
27. Berner, Eta S.; Tonya J.La Lande (2007). "1". Clinical Decision Support Systems: Theory and Practice (2 ed.). New York: Springer Science and Business Media. pp. 3–22.
28. Rothman, Brian; Joan. C. Leonard; Michael. M. Vigoda (2012). "Future of electronic health records: implications for decision support". Mount Sinai Journal of Medicine. 79 (6): 757–768. doi:10.1002/msj.21351. PMID 23239213.
29. Sambasivan, Murali; Pouyan Esmaeilzadeh; Naresh Kumar; Hossein Nezakati (2012). "Intention to adopt clinical decision support systems in a developing country: effect of Physician's perceived professional autonomy, involvement and belief: a cross-sectional study". BMC Medical Informatics and Decision Making. 12: 142–150. doi:10.1186/1472-6947-12-142. PMC 3519751. PMID 23216866.
30. Spellman Kennebeck, Stephanie; Nathan Timm; Michael K Farrell; S Andrew Spooner (2012). "Impact of electronic health record implementation on patient flow metrics in a pediatric emergency department". Journal of the American Medical Informatics Association. 19 (3): 443–447. doi:10.1136/amiajnl-2011-000462. PMC 3341791. PMID 22052897.
31. McGinn, Carrie Anna; Gagnon, Marie-Pierre; Shaw, Nicola; Sicotte, Claude; Mathieu, Luc; Leduc, Yvan; Grenier, Sonya; Duplantie, Julie; Abdeljelil, Anis Ben; Légaré, France (11 September 2012). "Users' perspectives of key factors to implementing electronic health records in Canada: a Delphi study". BMC Medical Informatics and Decision Making. 12 (1): 105. doi:10.1186/1472-6947-12-105. ISSN 1472-6947. PMC 3470948. PMID 22967231.
32. Rozenblum, R.; Jang, Y.; Zimlichman, E.; Salzberg, C.; Tamblyn, M.; Buckeridge, D.; Forster, A.; Bates, D. W.; Tamblyn, R. (22 February 2011). "A qualitative study of Canada's experience with the implementation of electronic health information technology". Canadian Medical Association Journal. 183 (5): E281–E288. doi:10.1503/cmaj.100856. ISSN 0820-3946. PMC 3060213. PMID 21343262.
33. Berner, Eta S.; Tonya J.La Lande (2007). "4". Clinical Decision Support Systems: Theory and Practice (2 ed.). New York: Springer Science and Business Media. pp. 64–98.
34. Loya, S. R.; Kawamoto, K; Chatwin, C; Huser, V (2014). "Service oriented architecture for clinical decision support: A systematic review and future directions". Journal of Medical Systems. 38 (12): 140. doi:10.1007/s10916-014-0140-z. PMC 5549949. PMID 25325996.
35. Charette, Robert N. (21 May 2012). "Troubled HealthSMART System Finally Cancelled in Victoria Australia". IEEE Spectrum. Retrieved 18 May 2013.
36. "EPAS program update". South Australian Health. Archived from the original on 26 June 2018. Retrieved 15 May 2013.
37. "Hospital beds to be cut by 840, South Australian Opposition calculates, but SA Health denies figure set". ABC News. 30 June 2015. Retrieved 26 July 2015.
38. "The eMR turns 10" (PDF). EHealth News. May–June 2016. Archived from the original (PDF) on 15 August 2016. Retrieved 6 August 2016.
39. "EBMEDS Clinical Decision Support". EBMEDS. Retrieved 12 February 2022.
40. "MEDRXIV From Concept to Reality: Examining India's Clinical Decision Support System (CDSS) Challenges & Opportunities". MEDRXIV. Retrieved 12 February 2024.
41. "Interactive dashboard identifies patients at risk of unsafe prescribing in a flexible and sustainable way". NIHR Evidence (Plain English summary). National Institute for Health and Care Research. 22 June 2020. doi:10.3310/alert_40404. S2CID 241368429.
42. Jeffries, Mark; Gude, Wouter T.; Keers, Richard N.; Phipps, Denham L.; Williams, Richard; Kontopantelis, Evangelos; Brown, Benjamin; Avery, Anthony J.; Peek, Niels; Ashcroft, Darren M. (17 April 2020). "Understanding the utilisation of a novel interactive electronic medication safety dashboard in general practice: a mixed methods study". BMC Medical Informatics and Decision Making. 20 (1): 69. doi:10.1186/s12911-020-1084-5. ISSN 1472-6947. PMC 7164282. PMID 32303219.

External links

• Duodecim EBMEDS Clinical Decision Support
• Decision support chapter from Coiera's Guide to Health Informatics
• OpenClinical Archived 2 February 2020 at the Wayback Machine maintains an extensive archive of Artificial Intelligence systems in routine clinical use.
• Robert Trowbridge/ Scott Weingarten. Chapter 53. Clinical Decision Support Systems
• Stanford CDSS

1. 1. 1. Clinical Decision Support Systems: Enhancing Healthcare Through Technology

In today's rapidly advancing healthcare landscape, clinical decision support systems (CDSS) play a pivotal role in improving patient care, enhancing clinical outcomes, and supporting healthcare professionals in making informed decisions. This article explores the concept, benefits, challenges, and future prospects of CDSS.

1. 1. 1. 1. What is a Clinical Decision Support System (CDSS)?

A Clinical Decision Support System (CDSS) is a computerized tool designed to assist healthcare providers in making clinical decisions by integrating medical knowledge with patient data. These systems utilize algorithms, databases, and patient information to provide tailored recommendations, alerts, and reminders to healthcare professionals at the point of care.

1. 1. 1. 1. Components of a CDSS:

1. **Knowledge Base**: Contains medical guidelines, protocols, best practices, and clinical rules.

2. **Patient Data Interface**: Integrates with electronic health records (EHR) systems to access patient demographics, medical history, test results, and current medications.

3. **Inference Engine**: Analyzes patient data and applies clinical rules to generate suggestions or alerts based on predefined algorithms.

4. **User Interface**: Presents recommendations, alerts, and relevant information to healthcare providers in a user-friendly format.

1. 1. 1. 1. Benefits of Clinical Decision Support Systems:

1. **Improved Clinical Decision Making**: CDSS provides evidence-based recommendations, reducing errors and variability in clinical practice.

2. **Enhanced Patient Safety**: Alerts for drug interactions, allergies, and potential adverse events help prevent medical errors and improve patient outcomes.

3. **Efficiency**: Streamlines workflow by providing quick access to relevant information, reducing the time spent on manual data retrieval and analysis.

4. **Cost-Effectiveness**: Helps in optimizing resource utilization, reducing unnecessary tests, treatments, and hospitalizations.

5. **Continuing Education**: Acts as a learning tool by keeping healthcare providers updated with the latest medical research and guidelines.

1. 1. 1. 1. Challenges in Implementing CDSS:

1. **Integration Complexity**: Integrating CDSS with existing EHR systems and workflows can be challenging and time-consuming.

2. **Data Quality and Interoperability**: Dependence on accurate and complete data is crucial for the effectiveness of CDSS.

3. **User Acceptance**: Resistance to change and unfamiliarity with new technology among healthcare providers.

4. **Alert Fatigue**: Overwhelming healthcare providers with excessive alerts and reminders, leading to desensitization.

5. **Legal and Ethical Issues**: Concerns regarding liability, privacy, and confidentiality of patient data.

1. 1. 1. 1. Future Trends and Innovations:

1. **Artificial Intelligence and Machine Learning**: Advanced algorithms for predictive analytics, personalized medicine, and real-time decision-making.

2. **Mobile and Cloud-based Solutions**: Remote access and seamless integration across different healthcare settings.

3. **Natural Language Processing**: Enhancing CDSS capabilities to interpret unstructured data such as clinical notes and imaging reports.

4. **Patient-Centered CDSS**: Involving patients in decision-making processes and personalized health management.

1. 1. 1. 1. Conclusion:

Clinical Decision Support Systems represent a transformative technology in healthcare, offering substantial benefits in clinical practice, patient safety, and healthcare efficiency. While challenges remain in implementation and adoption, ongoing advancements in technology and healthcare delivery are poised to further enhance the capabilities and impact of CDSS in improving overall healthcare outcomes.

In conclusion, CDSS are pivotal tools in the evolving landscape of healthcare technology, enabling healthcare professionals to leverage data-driven insights and medical knowledge effectively at the point of care, ultimately leading to better patient outcomes and enhanced healthcare delivery.