Computerized clinical decision support systems (CDSS)

Computerized clinical decision support systems (CDSS) are electronic tools that prompt provider behaviors in various areas of patient care, including medication ordering, chronic disease management, health care screening, and vaccination. CDSS can provide physicians, nurses, pharmacists, and other care providers with patient-specific prompts or warnings, treatment guidelines (e.g., order sets), automatic medication dosing calculators, or reports of overdue tests and medications as appropriate. These tools can be integrated with comprehensive electronic health record (EHR) systems, part of a computerized physician order entry (CPOE) system, or a standalone electronic interface; various types of CDSS exist for different workflows and settings, including primary, inpatient, acute, long term, and dental care.

Expected Beneficial Outcomes (Rated)

  • Improved processes of care

Other Potential Beneficial Outcomes

  • Increased appropriate drug prescribing

  • Improved health outcomes

  • Improved patient safety

Evidence of Effectiveness

There is strong evidence that computerized clinical decision support systems (CDSS) improve processes of care (, , Marasinghe 2015, CG-CVD, Bright 2012, Roshanov 2011, Roshanov 2011a, Sahota 2011, Souza 2011, Bryan 2008), particularly when used for drug prescribing and management (, , Jia 2016, Marasinghe 2015, , , Stultz 2012, AHRQ-McKibbon 2011, Hemens 2011, Nieuwlaat 2011, Sahota 2011, , Jamal 2009, Pearson 2009, Schedlbauer 2009, Wolfstadt 2008). Such systems have also been shown to improve patient outcomes in some circumstances (Varghese 2017, Borab 2017, Kooij 2017, , , Jia 2016, Moja 2014, Nieuwlaat 2011, Roshanov 2011a, Sahota 2011, Souza 2011, ). Additional evidence is needed to confirm effects on patient outcomes.

CDSS can improve processes of care in primary care (Bryan 2008), long term care (Marasinghe 2015), and acute care settings (Bennett 2017, Sahota 2011), especially when systems support preventive services, prescribing therapies, and ordering of clinical studies (Bright 2012) and tests (Roshanov 2011a). CDSS can also improve processes for chronic disease management (Roshanov 2011) and preventive care, such as screening and management of asthma (Fathima 2014), high cholesterol (Souza 2011), and cardiovascular disease (CG-CVD). In some circumstances, such systems can improve processes related to cancer screening and referrals, and mental health conditions (Souza 2011).

Clinical decision support systems can increase compliance with recommendations, guidelines, and protocols in medication prescription (, Borab 2017, , ), transplant care (), and laboratory test ordering (Delvaux 2017, Ennis 2015), and decrease inappropriate testing (, , ). CDSS may also benefit high risk groups, for example, decreasing inappropriate opioid prescribing among veterans () and increasing safety planning for youth at risk of suicide ().

CDSS can improve medication management (, AHRQ-McKibbon 2011), prescribing outcomes (Pearson 2009), and increase medication safety (Jia 2016, Marasinghe 2015). Combined with computerized provider order entry (CPOE), clinical decision support systems reduce medication prescribing errors (, Stultz 2012, Schedlbauer 2009) and may also reduce adverse drug events (Stultz 2012, Wolfstadt 2008). Medication dosing advice via CDSS can improve some patient outcomes (, Sahota 2011) and increase physician compliance with guidelines (, Jamal 2009, ). Pharmacy-specific CDSS that address potential safety concerns are more effective than CDSS that promote compliance with evidence-based guidelines (Robertson 2010).

Frequent CDSS alerts, especially inappropriate alerts, lead to “alert fatigue” and may cause users to miss or ignore important alerts (Fathima 2014, Stultz 2012, Moxey 2010). These point of care reminders are more likely to improve clinical processes of care when providers are required to enter a response (Shojania 2010). Clinical decision support systems can be less effective when doctors do not follow system advice ().

Clinical decision support systems have been shown to improve patient outcomes in some circumstances (Varghese 2017, Kooij 2017, , Roshanov 2011, Sahota 2011, Souza 2011, ). CDSS appear most likely to improve patient outcomes when used to help manage chronic conditions (Kooij 2017) such as high cholesterol (Souza 2011) and diabetes (, Nieuwlaat 2011). Such systems can improve blood glucose when implemented as part of a complex hospital-based intervention (). CDSS may also reduce adverse events (Varghese 2017, Borab 2017) and mortality in inpatient care (Varghese 2017).

Researchers suggest standardized content that can be easily updated and integration in an EHR to provide timely and appropriate support during workflows will increase the ability of CDSS to support diagnosis (Nurek 2015). The frequent need for customization on a site-by-site basis to meet organizational, departmental, and provider needs and practices, and lack of interoperability between different EHRs can be barriers to CDSS implementation (AHRQ-Mardon 2014). Recommendations for implementing CDSS and other e-health technologies include considering their complexity, adaptability, and compatibility with existing systems when selecting a system and involving key stakeholders early in the implementation process (Ross 2016).

The cost impacts of CDSS are unclear; some studies find decreases, some increases, and some no change in the cost of care (Jacob 2017, Kooij 2017, Fillmore 2013, Hemens 2011).

Impact on Disparities

No impact on disparities likely

Implementation Examples

Many commercially produced EHRs include CDSS (ONCHIT-CDS), and organizations such as Brigham and Women’s Hospital and the Yale School of Medicine developed decision support systems as part of the AHRQ Clinical Decision Support Demonstration Projects (AHRQ-Mardon 2014).

Partners Healthcare System, an integrated system of hospitals in Massachusetts, uses CDSS to provide real-time reminders on guideline-based care recommendations for acute respiratory infections, CAD, and diabetes (AHRQ HCIE-Middleton).

Implementation Resources

US DHHS-Meaningful use - US Department of Health and Human Services (US DHHS). Achieve meaningful use.

AHRQ-CDS - Agency for Healthcare Research and Quality (AHRQ). Health information technology: Clinical decision support (CDS).

CDC DHDSP-CDSS - Centers for Disease Control and Prevention (CDC), Division for Heart Disease and Stroke Prevention (DHDSP). Implementing clinical decision support systems (CDSS).

ONCHIT-Implementing HIT - The Office of the National Coordinator for Health Information Technology (ONCHIT). Implementing Health IT.

NAM-Optimizing CDS - Optimizing strategies for clinical decision support: Summary of a meeting series. Tcheng JE, Bakken S, Bates DW, et al., ed. Washington, DC: National Academy of Medicine. 2017.

Citations - Evidence

* Journal subscription may be required for access.

Bryan 2008 - Bryan C, Boren SA. The use and effectiveness of electronic clinical decision support tools in the ambulatory/primary care setting: A systematic review of the literature. Informatics in Primary Care. 2008;16:79-91.

Hemens 2011 - Hemens BJ, Holbrook A, Tonkin M, et al. Computerized clinical decision support systems for drug prescribing and management: A decision-maker-researcher partnership systematic review. Implementation Science. 2011;6(89):2-17.

Jamal 2009 - Jamal A, Mckenzie K, Clark M. The impact of health information technology on the quality of medical and health care: A systematic review. Health Information Management Journal. 2009;38(3):26-37

Moxey 2010 - Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson S-A. Computerized clinical decision support for prescribing: Provision does not guarantee uptake. Journal of the American Medical Informatics Association (JAMIA). 2010;17:25-33.

Nieuwlaat 2011 - Nieuwlaat R, Connolly SJ, Mackay J a, et al. Computerized clinical decision support systems for therapeutic drug monitoring and dosing: A decision-maker-researcher partnership systematic review. Implementation Science. 2011;6(90):1-14.

Nirantharakumar 2011* - Nirantharakumar K, Chen YF, Marshall T, Webber J, Coleman JJ. Clinical decision support systems in the care of inpatients with diabetes in non-critical care setting: Systematic review. Diabetic Medicine. 2011;29(6):698-708.

Pearson 2009 - Pearson S-A, Moxey A, Robertson J, et al. Do computerised clinical decision support systems for prescribing change practice: A systematic review of the literature (1990-2007). BMC Health Services Research. 2009;9(154):1-14.

Robertson 2010 - Robertson J, Walkom E, Pearson S, Hains I. The impact of pharmacy computerised clinical decision support on prescribing, clinical and patient outcomes: A systematic review of the literature. International Journal of Pharmacy Practice. 2010;18:69-87.

Roshanov 2011 - Roshanov PS, You JJ, Dhaliwal J, et al. Can computerized clinical decision support systems improve practitioners’ diagnostic test ordering behavior: A decision-maker-researcher partnership systematic review. Implementation Science. 2011;6(88):1-12.

Roshanov 2011a - Roshanov PS, Misra S, Gerstein HC, et al. Computerized clinical decision support systems for chronic disease management: A decision-maker-researcher partnership systematic review. Implementation Science. 2011;6(92):1-16.

Sahota 2011 - Sahota N, Lloyd R, Ramakrishna A, et al. Computerized clinical decision support systems for acute care management: A decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. Implementation Science. 2011;6(91):1-14.

Souza 2011 - Souza NM, Sebaldt RJ, Mackay J a, et al. Computerized clinical decision support systems for primary preventive care: A decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. Implementation Science. 2011;6(1):87.

Tawadrous 2011* - Tawadrous D, Shariff SZ, Haynes RB, Iansavichus A V, Jain AK, Garg AX. Use of clinical decision support systems for kidney-related drug prescribing: A systematic review. American Journal of Kidney Diseases. 2011;58(6):903-914.

AHRQ-McKibbon 2011 - McKibbon KA, Lokker C, Handler SM, et al. Enabling medication management through health information technology. Rockville: Agency for Healthcare Research and Quality (AHRQ); 2011.

Bright 2012 - Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decision-support systems. Annals of Internal Medicine. 2012;157(1):2-42.

Cochrane-Gillaizeau 2013* - Gillaizeau F, Chan E, Trinquart L, et al. Computerized advice on drug dosage to improve prescribing practice. Cochrane Database of Systematic Reviews. 2013;(11):CD002894.

Fillmore 2013 - Fillmore CL, Bray BE, Kawamoto K. Systematic review of clinical decision support interventions with potential for inpatient cost reduction. BMC Medical Informatics and Decision Making. 2013;13(135):1-9.

Georgiou 2007* - Georgiou A, Williamson M, Westbrook JI, Ray S. The impact of computerised physician order entry systems on pathology services: A systematic review. International journal of medical informatics. 2007;76(7):514-29.

Ranji 2014* - Ranji SR, Rennke S, Wachter RM. Computerised provider order entry combined with clinical decision support systems to improve medication safety: A narrative review. BMJ quality & safety. 2014;23(9):773-80.

Schedlbauer 2009 - Schedlbauer A, Prasad V, Mulvaney C, et al. What evidence supports the use of computerized alerts and prompts to improve clinicians' prescribing behavior. Journal of the American Medical Informatics Association. 2009;16(4):531-538.

Shojania 2010 - Shojania KG, Jennings A, Mayhew A, Ramsay C, Eccles M, Grimshaw J. Effect of point-of-care computer reminders on physician behaviour: A systematic review. Canadian Medical Association Journal. 2010;182(5):1-10.

Stultz 2012 - Stultz JS, Nahata MC. Computerized clinical decision support for medication prescribing and utilization in pediatrics. Journal of the American Medical Informatics Association. 2012;19:942-53.

Wolfstadt 2008 - Wolfstadt JI, Gurwitz JH, Field TS, et al. The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: A systematic review. Journal of General Internal Medicine. 2008;23(4):451-458.

CG-CVD - The Guide to Community Preventive Services (The Community Guide). Cardiovascular disease (CVD).

Ali 2016* - Ali SM, Giordano R, Lakhani S, Walker DM. A review of randomized controlled trials of medical record powered clinical decision support system to improve quality of diabetes care. International Journal of Medical Informatics. 2016;87:91-100.

Baysari 2016* - Baysari MT, Lehnbom EC, Li L, Hargreaves A, Day RO, Westbrook JI. The effectiveness of information technology to improve antimicrobial prescribing in hospitals: A systematic review and meta-analysis. International Journal of Medical Informatics. 2016;92:15-34.

Bennett 2017 - Bennett P, Hardiker NR. The use of computerized clinical decision support systems in emergency care: A substantive review of the literature. Journal of the American Medical Informatics Association. 2017;24(3):655-668.

Borab 2017 - Borab ZM, Lanni MA, Tecce MG, Pannucci CJ, Fischer JP. Use of computerized clinical decision support systems to prevent venous thromboembolism in surgical patients a systematic review and meta-analysis. JAMA Surgery. 2017;152(7):638-645.

Delvaux 2017 - Delvaux N, Van Thienen K, Heselmans A, de Velde S Van, Ramaekers D, Aertgeerts B. The effects of computerized clinical decision support systems on laboratory test ordering: A systematic review. Archives of Pathology & Laboratory Medicine. 2017;141(4):585-595.

Ennis 2015 - Ennis J, Gillen D, Rubenstein A, et al. Clinical decision support improves physician guideline adherence for laboratory monitoring of chronic kidney disease: A matched cohort study. BMC Nephrology. 2015;16:163.

Fathima 2014 - Fathima M, Peiris D, Naik-Panvelkar P, Saini B, Armour CL. Effectiveness of computerized clinical decision support systems for asthma and chronic obstructive pulmonary disease in primary care: A systematic review. BMC Pulmonary Medicine. 2014;14:189.

Felcher 2017* - Felcher AH, Gold R, Mosen DM, Stoneburner AB. Decrease in unnecessary vitamin D testing using clinical decision support tools: Making it harder to do the wrong thing. Journal of the American Medical Informatics Association. 2017;24(4):776-780.

Hendrickson 2018* - Hendrickson MA, Wey AR, Gaillard PR, Kharbanda AB. Implementation of an electronic clinical decision support tool for pediatric appendicitis within a hospital network. Pediatric Emergency Care. 2018;34(1):10-16.

Jacob 2017 - Jacob V, Thota AB, Chattopadhyay SK, et al. Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: A community guide systematic review. Journal of the American Medical Informatics Association. 2017;24(3):669-676.

Jia 2016 - Jia P, Zhang L, Chen J, Zhao P, Zhang M. The effects of clinical decision support systems on medication safety: An overview. PLoS ONE. 2016;11(12): e0167683.

Kooij 2017 - Kooij L, Groen WG, Van Harten WH. The effectiveness of information technology-supported shared care for patients with chronic disease: A systematic review. Journal of Medical Internet Research. 2017;19(6):e221.

Marasinghe 2015 - Marasinghe KM. Computerised clinical decision support systems to improve medication safety in long-term care homes: A systematic review. BMJ Open. 2015;5:e006539.

AHRQ-Mardon 2014 - Mardon R, Mercincavage L, Johnson M, et al. Findings and lessons from AHRQ's clinical decision support demonstration projects. Rockville: Agency for Healthcare Research and Quality (AHRQ); 2014.

Moja 2014 - Moja L, Kwag KH, Lytras T, et al. Effectiveness of computerized decision support systems linked to electronic health records: A systematic review and meta-analysis. American Journal of Public Health. 2014;104(12):e12-e22.

Niazkhani 2017* - Niazkhani Z, Pirnejad H, Rashidi Khazaee P. The impact of health information technology on organ transplant care: A systematic review. International Journal of Medical Informatics. 2017;100:95-107.

Nurek 2015 - Nurek M, Kostopoulou O, Delaney BC, Esmail A. Reducing diagnostic errors in primary care. A systematic meta-review of computerized diagnostic decision support systems by the LINNEAUS collaboration on patient safety in primary care. European Journal of General Practice. 2015;21(sup1):8-13.

Patel 2017* - Patel S, Carmichael JM, Taylor JM, Bounthavong M, Higgins DT. Evaluating the impact of a clinical decision support tool to reduce chronic opioid dose and decrease risk classification in a veteran population. Annals of Pharmacotherapy. 2017;52(4):325-331.

Reyes-Portillo 2018* - Reyes-Portillo JA, Chin EM, Toso-Salman J, Blake Turner J, Vawdrey D, Mufson L. Using electronic health record alerts to increase safety planning with youth at-risk for suicide: A non-randomized trial. Child and Youth Care Forum. 2018;47(3):391-402.

Ross 2016 - Ross J, Stevenson F, Lau R, Murray E. Factors that influence the implementation of e-health: A systematic review of systematic reviews (an update). Implementation Science. 2016;11:146.

Simpao 2017* - Simpao AF, Tan JM, Lingappan AM, Gálvez JA, Morgan SE, Krall MA. A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems. Journal of Clinical Monitoring and Computing. 2017;31(5):885-894.

Varghese 2017 - Varghese J, Kleine M, Gessner SI, Sandmann S, Dugas M. Effects of computerized decision support system implementations on patient outcomes in inpatient care: A systematic review. Journal of the American Medical Informatics Association. 2017;25(5):593-602.

White 2017* - White DR, Hamilton KW, Pegues DA, Hanish A, Umscheid CA. The impact of a computerized clinical decision support tool on inappropriate clostridium difficile testing. Infection Control & Hospital Epidemiology. 2017;38(10):1204-1208.

Citations - Implementation Examples

* Journal subscription may be required for access.

AHRQ HCIE-Middleton - Middleton B. Real-time decision and documentation support increases adherence to recommended care for respiratory infections, diabetes, and heart disease. Rockville: AHRQ Health Care Innovations Exchange.

ONCHIT-CDS - The Office of the National Coordinator for Health Information Technology (ONCHIT). Clinical decision support (CDS).

AHRQ-Mardon 2014 - Mardon R, Mercincavage L, Johnson M, et al. Findings and lessons from AHRQ's clinical decision support demonstration projects. Rockville: Agency for Healthcare Research and Quality (AHRQ); 2014.

Date Last Updated

Jun 28, 2018