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Purpose: To assess the extent to which clinical rules (CRs) can be implemented for automatic evaluation of quality of care in general practice.Methods: We assessed 81 clinical rules (CRs) adapted from a subset of Assessing Care of Vulnerable Elders (ACOVE) clinical rules, against Dutch College of General Practitioners (NHG) data model. Each CR was analyzed using the Logical Elements Rule METHOD: (LERM). LERM is a stepwise method of assessing and formalizing clinical rules for decision support. Clinical rules that satisfied the criteria outlined in the LERM method were judged to be implementable in automatic evaluation in general practice.Results: Thirty-three out of 81 (40.7%) Dutch-translated ACOVE clinical rules can be automatically evaluated in electronic medical record systems. Seven out of 7 CRs (100%) in the domain of diabetes can be automatically evaluated, 9/17 (52.9%) in medication use, 5/10 (50%) in depression care, 3/6 (50%) in nutrition care, 6/13 (46.1%) in dementia care, 1/6 (16.6%) in end of life care, 2/13 (15.3%) in continuity of care, and 0/9 (0%) in the fall-related care. Lack of documentation of care activities between primary and secondary health facilities and ambiguous formulation of clinical rules were the main reasons for the inability to automate the clinical rules.Conclusion: Approximately two-fifths of the primary care Dutch ACOVE-based clinical rules can be automatically evaluated. Clear definition of clinical rules, improved GP database design and electronic linkage of primary and secondary healthcare facilities can improve prospects of automatic assessment of quality of care. These findings are relevant especially because the Netherlands has very high automation of primary care. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Our advanced clinical decision support (CDS) system, entitled ‘adverse drug event alerting system’ (ADEAS), is in daily use in our hospital pharmacy. It is used by hospital pharmacists to select patients at risk of possible adverse drug events (ADEs). The system retrieves data from several information systems, and uses clinical rules to select the patients at risk of ADEs. The clinical rules are all medication related and are formulated using seven risk categories. Objective: This studies objectives are to 1) evaluate the use of the CDS system ADEAS in daily hospital pharmacy practice, and 2) assess the rule effectiveness and positive predictive value (PPV) of the clinical rules incorporated in the system. Setting: Leiden University Medical Center, The Netherlands. All patients admitted on six different internal medicine and cardiology wards were included. Measures: Outcome measures were total number of alerts, number of patients with alerts and the outcome of these alerts: whether the hospital pharmacist gave advice to prevent a possible ADE or not. Both overall rule effectiveness and PPV and rule effectiveness and PPV per clinical rule risk category were scored. Study design: During a 5 month study period safety alerts were generated daily by means of ADEAS. All alerts were evaluated by a hospital pharmacist and if necessary, healthcare professionals were subsequently contacted and advice was given in order to prevent possible ADEs. Results: During the study period ADEAS generated 2650 safety alerts in 931 patients. In 270 alerts (10%) the hospital pharmacist contacted the physician or nurse and in 204 (76%) cases this led to an advice to prevent a possible ADE. The remaining 2380 alerts (90%) were scored as non-relevant. Most alerts were generated with clinical rules linking pharmacy and laboratory data (1685 alerts). The overall rule effectiveness was 0.10 and the overall PPV was 0.08. Combination of rule effectiveness and PPV was highest for clinical rules based upon the risk category “basic computerized physician order entry (CPOE) medication safety alerts fine-tuned to high risk patients” (rule efficiency=0.17; PPV=0.14). Conclusion: ADEAS can effectively be used in daily hospital pharmacy practice to select patients at risk of potential ADEs, but to increase the benefits for routine patient care and to increase efficiency, both rule effectiveness and PPV for the clinical rules should be improved. Furthermore, clinical rules would have to be refined and restricted to those categories that are potentially most promising for clinical relevance, i.e. “clinical rules with a combination of pharmacy and laboratory data” and “clinical rules based upon the basic CPOE medication safety alerts fine-tuned to high risk patients”. [Copyright &y& Elsevier]

Abstract: Background: In intensive care environments, technology is omnipresent whereby ensuring constant monitoring and the administration of critical drugs to unstable patients. A clinical decision support system (CDSS), with its widespread possibilities, can be a valuable tool in supporting adequate patient care. However, it is still unclear how decision support alerts should be presented to physicians and other medical staff to ensure that they are used most effectively. Objective: To determine the effect of four different alert presentation methods on alert compliance after the implementation of an advanced CDSS on the intensive care unit (ICU) in our hospital. Methods: A randomized clinical trial was executed from August 2010 till December 2011, which included all patients admitted to the ICU of our hospital. The CDSS applied contained a set of thirteen locally developed clinical rules. The percentage of alert compliance was compared for four alert presentation methods: pharmacy intervention, physician alert list, electronic health record (EHR) section and pop-up alerts. Additionally, surveys were held to determine the method most preferred by users of the CDSS. Results: In the study period, the CDSS generated 902 unique alerts, primarily due to drug dosing during decreased renal function and potassium disturbances. Alert compliance was highest for recommendations offered in pop-up alerts (41%, n =68/166), followed by pharmacy intervention (33%, n =80/244), the physician alert list (20%, n =40/199) and the EHR section (19%, n =55/293). The method most preferred by clinicians was pharmacy intervention, and pop-up alerts were found suitable as well if applied correctly. The physician alert list and EHR section were not considered suitable for CDSSs in the process of this study. Conclusion: The alert presentation method used for CDSSs is crucial for the compliance with alerts for the clinical rules and, consequently, for the efficacy of these systems. Active alerts such as pop-ups and pharmacy intervention were more effective than passive alerts, which do not automatically appear within the clinical workflow. In this pilot study, ICU clinicians also preferred pharmacy intervention and pop-up alerts. More research is required to expand these results to other departments and other hospitals, as well as to other types of CDSSs and different alert presentation methods. [Copyright &y& Elsevier]

Abstract: Objectives: The objectives of the study were as follows: (1) to investigate whether the predictive validity of a previously developed back pain prediction rule could be improved; (2) to determine if the rule can be shortened without loss of predictive validity; (3) to compare the rule with the physician''s judgment; (4) to assess, in a different population, its 2-year predictive validity; and (5) to evaluate the clinical applicability of the rule in a first-line care setting. Study Design and Setting: One thousand two hundred and sixty-two participants were enrolled in the study (participation: 91%) before a medical consultation for nonspecific back pain in a large emergency room and were followed up for 2 years (follow-up: 92.5%). The effects of adding new items and deleting any one of the original items were evaluated. The predictions by the rule and the physicians were compared with the 2-year actual functional limitations (measured with the Roland–Morris Disability Questionnaire). Results: Although the final prediction rule included only five items (feeling everything is an effort, trouble getting breath, hot/cold spells, numbness/tingling in parts of body, and pain in heart/chest), its predictive validity was greater than that of the original 17-item version and was superior to the physician''s prediction. The rule was easily applied. Conclusion: A five-item clinical prediction rule of long-term back-related functional limitations could help first-line care physicians to concentrate the clinical attention on patients at higher risk. [Copyright &y& Elsevier]