Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials

Objectives To identify factors that differentiate between effective and ineffective computerised clinical decision support systems in terms of improvements in the process of care or in patient outcomes. Design Meta-regression analysis of randomised controlled trials. Data sources A database of features and effects of these support systems derived from 162 randomised controlled trials identified in a recent systematic review. Trialists were contacted to confirm the accuracy of data and to help prioritise features for testing. Main outcome measures “Effective” systems were defined as those systems that improved primary (or 50% of secondary) reported outcomes of process of care or patient health. Simple and multiple logistic regression models were used to test characteristics for association with system effectiveness with several sensitivity analyses. Results Systems that presented advice in electronic charting or order entry system interfaces were less likely to be effective (odds ratio 0.37, 95% confidence interval 0.17 to 0.80). Systems more likely to succeed provided advice for patients in addition to practitioners (2.77, 1.07 to 7.17), required practitioners to supply a reason for over-riding advice (11.23, 1.98 to 63.72), or were evaluated by their developers (4.35, 1.66 to 11.44). These findings were robust across different statistical methods, in internal validation, and after adjustment for other potentially important factors. Conclusions We identified several factors that could partially explain why some systems succeed and others fail. Presenting decision support within electronic charting or order entry systems are associated with failure compared with other ways of delivering advice. Odds of success were greater for systems that required practitioners to provide reasons when over-riding advice than for systems that did not. Odds of success were also better for systems that provided advice concurrently to patients and practitioners. Finally, most systems were evaluated by their own developers and such evaluations were more likely to show benefit than those conducted by a third party.

[1]  C J McDonald,et al.  Computer predictions of abnormal test results. Effects on outpatient testing. , 1988, JAMA.

[2]  V. Preedy,et al.  Randomized Controlled Trial , 2010 .

[3]  Ewout W Steyerberg,et al.  Logistic regression modeling and the number of events per variable: selection bias dominates. , 2011, Journal of clinical epidemiology.

[4]  Robyn Tamblyn,et al.  The effectiveness of integrated health information technologies across the phases of medication management: a systematic review of randomized controlled trials , 2012, J. Am. Medical Informatics Assoc..

[5]  Patrick Royston,et al.  Multiple imputation using chained equations: Issues and guidance for practice , 2011, Statistics in medicine.

[6]  Thomas Agoritsas,et al.  Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. , 2011, Journal of clinical epidemiology.

[7]  R Brian Haynes,et al.  Computerized clinical decision support systems for chronic disease management: A decision-maker-researcher partnership systematic review , 2011, Implementation science : IS.

[8]  Roger B. Davis,et al.  Overrides of medication alerts in ambulatory care. , 2009, Archives of internal medicine.

[9]  C J McDonald,et al.  The Medical Gopher--a microcomputer system to help find, organize and decide about patient data. , 1986, The Western journal of medicine.

[10]  David W. Bates,et al.  Standard practices for computerized clinical decision support in community hospitals: a national survey , 2012, J. Am. Medical Informatics Assoc..

[11]  Adam Wright,et al.  A four-phase model of the evolution of clinical decision support architectures , 2008, Int. J. Medical Informatics.

[12]  Karim Keshavjee,et al.  Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial , 2009, Canadian Medical Association Journal.

[13]  Charles E. Leonard,et al.  Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial. , 2010, Archives of internal medicine.

[14]  C. Mc Donald,et al.  Use of a computer to detect and respond to clinical events: its effect on clinician behavior. , 1976, Annals of internal medicine.

[15]  C. McDonald,et al.  Clinical Decision Support Within the Regenstrief Medical Record System , 2007 .

[16]  J. Concato,et al.  A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.

[17]  Suzanne Bakken,et al.  The Effect of a Mobile Clinical Decision Support System on the Diagnosis of Obesity and Overweight in Acute and Primary Care Encounters , 2009, ANS. Advances in nursing science.

[18]  Alastair Baker,et al.  Crossing the Quality Chasm: A New Health System for the 21st Century , 2001, BMJ : British Medical Journal.

[19]  Marc Berg,et al.  Review Paper: Overriding of Drug Safety Alerts in Computerized Physician Order Entry , 2006, J. Am. Medical Informatics Assoc..

[20]  Diane L. Seger,et al.  A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems , 2010, J. Am. Medical Informatics Assoc..

[21]  E. Berner,et al.  Clinical Decision Support Systems: Theory and Practice , 1998 .

[22]  C J McDonald,et al.  The effect on test ordering of informing physicians of the charges for outpatient diagnostic tests. , 1990, The New England journal of medicine.

[23]  J. Habbema,et al.  Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. , 2001, Journal of clinical epidemiology.

[24]  J. Marc Overhage,et al.  Research Paper: A Randomized Trial of "Corollary Orders" to Prevent Errors of Omission , 1997, J. Am. Medical Informatics Assoc..

[25]  Kaveh G Shojania,et al.  Effect of point-of-care computer reminders on physician behaviour: a systematic review , 2010, Canadian Medical Association Journal.

[26]  E. Balas,et al.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success , 2005, BMJ : British Medical Journal.

[27]  M H Trivedi,et al.  Development and Implementation of Computerized Clinical Guidelines: Barriers and Solutions , 2002, Methods of Information in Medicine.

[28]  Reed M. Gardner,et al.  White Paper: Designing Medical Informatics Research and Library-Resource Projects to Increase What Is Learned , 1994, J. Am. Medical Informatics Assoc..

[29]  J. Hanley,et al.  The effect of omitted covariates on confidence interval and study power in binary outcome analysis: a simulation study. , 2007, Contemporary clinical trials.

[30]  Jeremy C. Wyatt,et al.  The case for randomized controlled trials to assess the impact of clinical information systems , 2011, J. Am. Medical Informatics Assoc..

[31]  S. Flottorp,et al.  Process evaluation of a cluster randomized trial of tailored interventions to implement guidelines in primary care--why is it so hard to change practice? , 2003, Family practice.

[32]  H. Lehmann,et al.  Clinical Decision Support Systems (cdsss) Have Been Hailed for Their Potential to Reduce Medical Errors Clinical Decision Support Systems for the Practice of Evidence-based Medicine , 2022 .

[33]  Michael A Babyak,et al.  What You See May Not Be What You Get: A Brief, Nontechnical Introduction to Overfitting in Regression-Type Models , 2004, Psychosomatic medicine.

[34]  Anne Holbrook,et al.  Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials , 2009, BMC Medical Informatics Decis. Mak..

[35]  E. Hing,et al.  Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001-2013. , 2014, NCHS data brief.

[36]  S J Rolnick,et al.  Lessons from experienced guideline implementers: attend to many factors and use multiple strategies. , 2000, The Joint Commission journal on quality improvement.

[37]  David W. Bates,et al.  Clinical decision support systems could be modified to reduce 'alert fatigue' while still minimizing the risk of litigation. , 2011, Health affairs.

[38]  Karim Keshavjee,et al.  Individualized Electronic Decision Support and Reminders Can Improve Diabetes Care in the Community , 2005, AMIA.

[39]  J. Marc Overhage,et al.  A plea for controlled trials in medical informatics , 1994, J. Am. Medical Informatics Assoc..

[40]  M. Schemper,et al.  A solution to the problem of separation in logistic regression , 2002, Statistics in medicine.

[41]  H. Mcdonald,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. , 2005, JAMA.

[42]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[43]  E A Balas,et al.  Improving preventive care by prompting physicians. , 2000, Archives of internal medicine.

[44]  R. Haynes,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision-maker-researcher partnership systematic review , 2010, Implementation science : IS.

[45]  C. Quinn,et al.  WellDoc mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction. , 2008, Diabetes technology & therapeutics.

[46]  D. Blumenthal,et al.  The "meaningful use" regulation for electronic health records. , 2010, The New England journal of medicine.

[47]  D. Firth Bias reduction of maximum likelihood estimates , 1993 .

[48]  J Marc Overhage,et al.  Can computer-generated evidence-based care suggestions enhance evidence-based management of asthma and chronic obstructive pulmonary disease? A randomized, controlled trial. , 2005, Health services research.

[49]  S. Thompson,et al.  How should meta‐regression analyses be undertaken and interpreted? , 2002, Statistics in medicine.

[50]  Richard W. Grant,et al.  Facilitated Lipid Management Using Interactive E-mail: Preliminary Results of a Randomized Controlled Trial , 2004, MedInfo.

[51]  Nancy L Wilczynski,et al.  Computerized clinical decision support systems for therapeutic drug monitoring and dosing: A decision-maker-researcher partnership systematic review , 2011, Implementation science : IS.

[52]  Thomas Wetter,et al.  Lessons learnt from bringing knowledge-based decision support into routine use , 2002, Artif. Intell. Medicine.

[53]  Jeremy C. Wyatt,et al.  Making electronic prescribing alerts more effective: scenario-based experimental study in junior doctors , 2011, J. Am. Medical Informatics Assoc..

[54]  M. Jaana,et al.  Prioritizing the Risk Factors Influencing the Success of Clinical Information System Projects , 2008, Methods of Information in Medicine.

[55]  Keith J. Petrie,et al.  Patients and computers as reminders to screen for diabetes in family practice , 2005, Journal of General Internal Medicine.

[56]  Tapabrata Maiti,et al.  A comparative study of the bias corrected estimates in logistic regression , 2008, Statistical methods in medical research.

[57]  Richard N. Shiffman,et al.  Model Formulation: A Design Model for Computer-based Guideline Implementation Based on Information Management Services , 1999, J. Am. Medical Informatics Assoc..

[58]  J. Eluf-Neto,et al.  A randomized controlled trial comparing a computer-assisted insulin infusion protocol with a strict and a conventional protocol for glucose control in critically ill patients. , 2009, Journal of critical care.

[59]  David Koff,et al.  Can computerized clinical decision support systems improve practitioners' diagnostic test ordering behavior? A decision-maker-researcher partnership systematic review , 2011 .

[60]  G. Barnett,et al.  Randomized controlled trial of an informatics-based intervention to increase statin prescription for secondary prevention of coronary disease , 2007, Journal of General Internal Medicine.

[61]  N. Wilczynski,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 , 2011, Implementation science : IS.

[62]  C. McDonald,et al.  Physician inpatient order writing on microcomputer workstations. Effects on resource utilization. , 1993, JAMA.

[63]  R Brian Haynes,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 , 2011, Implementation science : IS.

[64]  Brian J Hemens,et al.  Computerized clinical decision support systems for drug prescribing and management: A decision-maker-researcher partnership systematic review , 2011, Implementation science : IS.

[65]  Joseph Beyene,et al.  Determining relative importance of variables in developing and validating predictive models , 2009, BMC medical research methodology.

[66]  C. McDonald,et al.  Effects of computerized guidelines for managing heart disease in primary care , 2003, Journal of General Internal Medicine.

[67]  Roger B. Davis,et al.  Physicians' decisions to override computerized drug alerts in primary care. , 2003, Archives of internal medicine.