Computerised physician order entry-related medication errors: analysis of reported errors and vulnerability testing of current systems.

IMPORTANCE: Medication computerised provider order entry (CPOE) has been shown to decrease errors and is being widely adopted. However, CPOE also has potential for introducing or contributing to errors. OBJECTIVES: The objectives of this study are to (a) analyse medication error reports where CPOE was reported as a 'contributing cause' and (b) develop 'use cases' based on these reports to test vulnerability of current CPOE systems to these errors. METHODS: A review of medication errors reported to United States Pharmacopeia MEDMARX reporting system was made, and a taxonomy was developed for CPOE-related errors. For each error we evaluated what went wrong and why and identified potential prevention strategies and recurring error scenarios. These scenarios were then used to test vulnerability of leading CPOE systems, asking typical users to enter these erroneous orders to assess the degree to which these problematic orders could be entered. RESULTS: Between 2003 and 2010, 1.04 million medication errors were reported to MEDMARX, of which 63 040 were reported as CPOE related. A review of 10 060 CPOE-related cases was used to derive 101 codes describing what went wrong, 67 codes describing reasons why errors occurred, 73 codes describing potential prevention strategies and 21 codes describing recurring error scenarios. Ability to enter these erroneous order scenarios was tested on 13 CPOE systems at 16 sites. Overall, 298 (79.5%) of the erroneous orders were able to be entered including 100 (28.0%) being 'easily' placed, another 101 (28.3%) with only minor workarounds and no warnings. CONCLUSIONS AND RELEVANCE: Medication error reports provide valuable information for understanding CPOE-related errors. Reports were useful for developing taxonomy and identifying recurring errors to which current CPOE systems are vulnerable. Enhanced monitoring, reporting and testing of CPOE systems are important to improve CPOE safety.

An industrial process view of information delivery to support clinical decision making: implications for systems design and process measures.

Clinical decision making is driven by information in the form of patient data and clinical knowledge. Currently prevalent systems used to store and retrieve this information have high failure rates, which can be traced to well-established system constraints. The authors use an industrial process model of clinical decision making to expose the role of these constraints in increasing variability in the delivery of relevant clinical knowledge and patient data to decision-making clinicians. When combined with nonmodifiable human cognitive and memory constraints, this variability in information delivery is largely responsible for the high variability of decision outcomes. The model also highlights the supply characteristics of information, a view that supports the application of industrial inventory management concepts to clinical decision support. Finally, the clinical decision support literature is examined from a process-improvement perspective with a focus on decision process components related to information retrieval. Considerable knowledge gaps exist related to clinical decision support process measurement and improvement.

The impact of anticipatory patient data displays on physician decision making: a pilot study.

Computerized patient records have long offered the promise of facilitated access to patient data for clinical decision-making. Nonetheless, the decision process benefits of improved patient data access have been poorly quantified by prior informatics research. We conducted a pilot study to test the feasibility of study methods and gather data for the planning of a future clinical trial designed to assess the impact of patient data summary displays on serum lipid test interpretation time, on targeted data retrieval time for related data, and on decision quality. The pilot demonstrated feasibility and high face validity of the decision-making simulation methods used. Problem-focused patient data summaries appear to reduce time-based decision performance measures by 40-50%, and may improve decision quality even without the inclusion of knowledge-based recommendations or guideline representations.

Computerized patient records in primary care. Their role in mediating guideline-driven physician behavior change.

Implementation of practice guidelines remains problematic in spite of enormous efforts to develop and disseminate them, to establish their credibility, and to create incentives for physicians to adopt them. These strategies have failed to systematically change physician behavior because they do not address the involuntary time and mental processing constraints that have been clearly demonstrated to hamper physicians' ability to comply with guidelines. Computerized patient record systems directly address these constraints, and evidence is mounting that they are effective tools for changing physician behavior. A properly configured computerized patient record system provides decision support, facilitates work flow, and enables the routine collection of data for performance feedback. A synthesis of relevant research from the domains of practice guidelines and medical informatics strongly suggests that the operational support provided by computerized patient record systems will have a major impact on physician compliance with practice guidelines.

Computerized decision support systems in primary care.

Computerized decision support can be passive or active. Passive decision support occurs when a computer facilitates access to relevant patient data or clinical knowledge for interpretation by the physician. Examples include CPR systems and reference texts or literature databases on CD-ROM. Effective passive decision support may ultimately prove to have a significant impact on physician decision making, but its potential to do so has been largely unexplored. Active decision support implies some higher level of information processing, or inference, by the computer. Examples include reminder / alert systems and diagnostic decision support systems. Inference processing in active decision support systems is generally rule-based, but probabilistic inference has been successfully used as well. Reminder systems have been consistently demonstrated to improve dramatically physician guideline compliance, generally by reducing oversight or error. The same potential for large-scale, systematic impact on physician decision-making by diagnostic decision support systems probably does not exist, but these systems may prove to be extremely useful in individual cases. Current applicability of diagnostic decision support systems to primary care is limited by the incompleteness and inaccuracies of the knowledge bases of these systems with respect to primary care. The applicability of computerized decision support in general to primary care is limited by more practical considerations. Widespread computerized decision support will not occur without CPR systems coupled with appropriate data standards and nomenclatures that will permit decision support tools to be accessed effortlessly during the routine process of patient care.

Dietitian practices for adult outpatients with hypercholesterolemia referred by physicians. The Minnesota Dietitian Survey.

OBJECTIVE: To characterize the physician-to-dietitian referral process and dietitian practice patterns and opinions related to adult outpatients with hypercholesterolemia. DESIGN: Cross-sectional mail survey. PARTICIPANTS: Minnesota dietitians who treat adult outpatients referred by physicians for hypercholesterolemia. MAIN OUTCOME MEASURES: Proportion of patients for whom background information or specific diet instructions were provided by referring physicians and for whom follow-up was recommended and dietary fat content calculated by the dietitians. RESULTS: Completed questionnaires were returned by 216 of 267 eligible dietitians (81% response rate). Respondents saw, on average, 31 referred patients per month, 31% of whom were referred for hypercholesterolemia, 31% for type II diabetes mellitus, and 24% for obesity. For patients referred for hypercholesterolemia, background information was provided by physicians 37% of the time, and specific diet instructions 15% of the time. One or more follow-up visits by the dietitians were recommended for 42% of patients referred for hypercholesterolemia, compared with 60% and 70% of patients referred for diabetes and obesity, respectively. The average number of additional visits within 6 months of the initial consultation recommended by dietitians was 2.0 for patients referred for hypercholesterolemia, 3.5 for patients referred for diabetes, and 6.7 for patients referred for obesity, and the number of visits that occurred was half or less than that recommended. Dietary fat content was calculated by the dietitians for only 25% of patients referred for hypercholesterolemia. CONCLUSIONS: For adult outpatients referred to dietitians for hypercholesterolemia, relevant patient information is usually not provided by referring physicians, the number of follow-up visits is well below what would reasonably be expected to produce significant and sustained eating behavior change, and calculation of dietary fat content is generally not done. More research is needed to determine appropriate nutrition intervention protocols for these patients.