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Commentary: Real-Time Computerized Support Systems
John M. Davis, M.D.; Daniel J. Luchins, M.D.
Psychiatric Services 2009; doi: 10.1176/appi.ps.60.12.1603
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Dr. Davis is affiliated with the Psychiatric Institute, Department of Psychiatry, University of Illinois, Chicago, 1601 West Taylor St. (M/C 912), Chicago, IL 60612 (e-mail: jdavis@psych.uic.edu). Dr. Luchins is with the Jesse Brown Veterans Affairs Medical Center, Chicago.

In this issue of Psychiatric Services, Susan M. Essock, Ph.D., and colleagues report on a study that used New York State Medicaid claims data to document questionable psychotropic prescribing practices, such as prescription of antipsychotics that produce weight gain for patients with preexisting cardiometabolic disorders, accompanied by failure to carry out necessary metabolic monitoring. But can these data be used to improve prescribing practices?

Medicaid claims data have been used in other states to generate individualized "Dear Prescriber" letters (1), but these are sent weeks or months after the prescription was written. The American Recovery and Reinvestment Act (the stimulus bill) may make real-time feedback based on computerized prescription data a reality. The act contained almost $30 billion for nationwide adoption of electronic medical record systems that can link prescriber, laboratory, and pharmacy data with other patient-specific data, such as age, weight, comorbid conditions, and other medications. Such linkage could provide prescribing physicians with useful information while they are still with their patient, flagging potential problems and providing links to frequently asked questions, relevant guidelines, articles, and sources of consultation. The system could provide information that physicians are not aware of and serve as a vehicle for disseminating new clinical information. Because such a system would have access to multiple sources of patient-specific information, it might generate fewer false-positive flags or messages than systems that do not take into account patient-specific variables (2).

The system could also be used to promote medication adherence by automatically generating a message (Internet or phone) to a patient when a refill is due and notifying the treatment team if the medication is not picked up. Because the most common reason for treatment failure is nonadherence, early detection can provide an opportunity for adherence support or a switch to a depot medication before the patient's condition deteriorates. Both controlled and mirror-image studies suggest that depot medications can reduce relapse and readmission by about 80% in such nonadherent populations (3).

However, there are limitations—in helping physicians understand, accept, and use such systems and in overcoming an array of technical problems. Empirical studies suggest that such computerized systems have only modest effects on physician practice (4) or clinical outcomes (5) and can generate their own errors (6). Such systems can also become dictatorial. There is a temptation to use them to force compliance with guidelines, such as blocking an olanzapine refill if metabolic tests were not ordered or if documentation was not entered in the chart. Such invasive procedures must be used with caution. People, unlike billiard balls, don't just have things done to them—they act back. Clinicians can react either by following the computerized prompts or by simply not ordering the recommended drug, even if it is the optimal choice. The promise of such computerized prescription support (a click away) will be realized only when clinicians and not just administrators perceive it as useful.

At the level of the hospital or health care system, such computerized monitoring can be used for quality control. Experiences in other settings, such as industry and the military, suggest that the most important function of quality control lies in not simply identifying the error after it occurs but in early identification, determination of causation, and correction of the underlying cause. That said, even with reams of computerized data it requires considerable knowledge of both medicine and administration to find and fix underlying causes in a way that does not create greater problems.

To successfully implement computerized support systems, we must empirically examine their real-world effectiveness and potential for untoward effects and not assume that a new technology guarantees progress.

Dr. Luchins is a consultant to Express Scripts. Dr. Davis reports no competing interests.

Bronze Award: Missouri Mental Health Medicaid Pharmacy Partnership Project: a successful partnership to improve prescribing practices. Psychiatric Services 57:1528–1529, 2006
 
Walters CL, Davidowitz NO, Heineken PA, et al: Pitfalls of converting practice guidelines into quality measures. JAMA 291:2466–2470, 2007
 
Davis JM, Metalon L, Watanabe MD, et al: Depot antipsychotic drugs' place in therapy. Drugs 47:741–773, 1994
 
Judge J, Fields TS, DeForio M, et al: Prescribers' response to alerts during medication ordering in long term care settings. Journal of the American Medical Informatics Association 13:385–39, 2006
 
Garg AX, Adhikari NKJ, McDonald H, et al: Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293:1223–1238, 2005
 
Ash JS, Berg M, Coiera E: Some unintended consequences of information technology in health systems: the nature of patient care information system-related errors. JAMA 11:104–112, 2004
 
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References

Bronze Award: Missouri Mental Health Medicaid Pharmacy Partnership Project: a successful partnership to improve prescribing practices. Psychiatric Services 57:1528–1529, 2006
 
Walters CL, Davidowitz NO, Heineken PA, et al: Pitfalls of converting practice guidelines into quality measures. JAMA 291:2466–2470, 2007
 
Davis JM, Metalon L, Watanabe MD, et al: Depot antipsychotic drugs' place in therapy. Drugs 47:741–773, 1994
 
Judge J, Fields TS, DeForio M, et al: Prescribers' response to alerts during medication ordering in long term care settings. Journal of the American Medical Informatics Association 13:385–39, 2006
 
Garg AX, Adhikari NKJ, McDonald H, et al: Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293:1223–1238, 2005
 
Ash JS, Berg M, Coiera E: Some unintended consequences of information technology in health systems: the nature of patient care information system-related errors. JAMA 11:104–112, 2004
 
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