Use of an automated pharmacy system and patient registries to recruit HMO enrollees for an influenza campaign.
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OBJECTIVE: To develop methods to identify, recruit, and vaccinate HMO enrollees at increased risk for influenza-related complications as part of a comprehensive influenza campaign. SETTING: Group Health Cooperative (GHC) is a large, membership-governed, managed care organization that serves 395,000 members in the Puget Sound area. APPROACH: An automated pharmacy system and patient data registries were used to identify enrollees with chronic illness. Enrollees with chronic illness and all enrollees 65 years of age and older were considered "high-risk" enrollees to be recruited for vaccination. Postcard reminders coupled with a publicity and education campaign were used to recruit high-risk enrollees to special influenza clinics. RESULTS: Our approach identified 2.5% of children (persons < 18 years of age) and 10.5% of adults (persons 18 to 65 years of age) as chronically ill and thus at high risk for influenza-related complications. Most high-risk children were identified through prescriptions for steroids, autonomic inhalers, or both or because they were enrolled in the asthma registry. Most high-risk adults were identified because of prescriptions for steroids, insulin, or oral hypoglycemic agents; because they had received pneumococcal vaccine; or because they were enrolled in the diabetes registry. Influenza coverage rates for all seniors (persons > or = 65 years of age) increased from 34% in 1984 to more than 72% in the 1996-1997 campaign year. Coverage rates were much lower for high-risk children (36%) and adults (46%). CONCLUSIONS: Influenza coverage rates can still be substantially improved for adults younger than 65 years of age and children at high risk for influenza-related complications. (+info)
The impact of computerized physician order entry on medication error prevention.
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BACKGROUND: Medication errors are common, and while most such errors have little potential for harm they cause substantial extra work in hospitals. A small proportion do have the potential to cause injury, and some cause preventable adverse drug events. OBJECTIVE: To evaluate the impact of computerized physician order entry (POE) with decision support in reducing the number of medication errors. DESIGN: Prospective time series analysis, with four periods. SETTING AND PARTICIPANTS: All patients admitted to three medical units were studied for seven to ten-week periods in four different years. The baseline period was before implementation of POE, and the remaining three were after. Sophistication of POE increased with each successive period. INTERVENTION: Physician order entry with decision support features such as drug allergy and drug-drug interaction warnings. MAIN OUTCOME MEASURE: Medication errors, excluding missed dose errors. RESULTS: During the study, the non-missed-dose medication error rate fell 81 percent, from 142 per 1,000 patient-days in the baseline period to 26.6 per 1,000 patient-days in the final period (P < 0.0001). Non-intercepted serious medication errors (those with the potential to cause injury) fell 86 percent from baseline to period 3, the final period (P = 0.0003). Large differences were seen for all main types of medication errors: dose errors, frequency errors, route errors, substitution errors, and allergies. For example, in the baseline period there were ten allergy errors, but only two in the following three periods combined (P < 0.0001). CONCLUSIONS: Computerized POE substantially decreased the rate of non-missed-dose medication errors. A major reduction in errors was achieved with the initial version of the system, and further reductions were found with addition of decision support features. (+info)
Notification of real-time clinical alerts generated by pharmacy expert systems.
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We developed and implemented a strategy for notifying clinical pharmacists of alerts generated in real-time by two pharmacy expert systems: one for drug dosing and the other for adverse drug event prevention. Display pagers were selected as the preferred notification method and a concise, yet readable, format for displaying alert data was developed. This combination of real-time alert generation and notification via display pagers was shown to be efficient and effective in a 30-day trial. (+info)
An information system to promote intravenous-to-oral medication conversion.
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Many inpatients remain on expensive intravenous medications, even after they become able to take bioequivalent oral alternatives. We developed a computer intervention to identify such patients and to deliver alerts suggesting a switch to the oral medication. In the first phase of the project, alerts were delivered to pharmacists. The Brigham Integrated Computer System (BICS) was used to produce a daily report of patients receiving any of six targeted intravenous medications, who also had orders for an oral diet or other scheduled oral medications. Staff pharmacists screened the report and suggested IV to PO conversion in appropriate cases to the patient's nurses and/or physicians. Feedback was documented in the BICS system. Analysis of the pilot study showed that in 31.7% of cases, physicians agreed to change (or had just changed) the patient's medication from IV to PO. Further analysis of pilot (Phase I) data was performed against a variety of parameters in order to increase the fraction of alerts deemed appropriate for conversion. These more specific alerts can be sent directly to physicians. (+info)
Unsupervised knowledge discovery in medical databases using relevance networks.
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Increasing amounts of data exist in medical databases. When multiple variables are measured for each case in a data set, there exists an underlying relationship between all pairs of variables, some highly correlated and some not. This report describes a technique that creates networks of related variables, or relevance networks, by dropping links with either too weak correlation or too few data points to defend the relationship. The paper describes how applying this methodology to the domain of laboratory results allows the generation of meaningful relations between types of laboratory tests. These relations could be used as the basis of further exploratory research. (+info)
Feasibility study of multicentre comparison of NHS hospital pharmacy computer data.
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AIMS: This study aims to determine the feasibility of collecting, collating and analysing drug expenditure data from a sample of acute hospitals in England. METHODS: The hospital pharmacy computer system was used to report on drug expenditure from 16 hospitals throughout England for a 2 year period. These data were analysed as a whole and hospital episode statistics were correlated to hospital drug costs. RESULTS: Hospital outpatient costs were found to be approximately one third of hospital inpatient costs. Cardiovascular drugs accounted for the greatest increase in expenditure for both inpatients and outpatients (25%). The most expensive therapeutic area of drug use across all sites was anti-infectives. The average daily number of occupied beds explained 55% of the variation in inpatient expenditure and the number of outpatient (including Accident and Emergency) attendances explained 60% of the outpatient drug expenditure. CONCLUSIONS: This project has confirmed the feasibility of collecting, collating and analysing hospital drug expenditure and identified some interesting patterns and trends in hospital drug use. Hospital activity is reflected in hospital drug costs. (+info)
Detection and incidence of drug-induced liver injuries in hospital: a prospective analysis from laboratory signals.
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AIMS: Liver damage remains the most frequent type of adverse drug reaction (ADRs) that can lead to the withdrawal of a drug from the market. The abnormal laboratory data identified by computerized hospital information systems can be used in order to improve the detection of ADRs. Our objectives were to assess the detection and incidence of drug-induced liver abnormalities in a university hospital inpatient population and to evaluate the underreporting rate of drug-induced liver injury. METHODS: We conducted a prospective study performed 1 week per month from June to October 1997. We selected patients by a computerized process using biochemistry laboratory data, based on serum enzyme values, alanine aminotransferase (over 2 fold normal) and alkaline phosphatase (over 1.5 fold normal). RESULTS: Among 1976 ALT and 1814 AP assays performed during the period of the study, 156 (7.9%) and 159 (8.8%) tests, respectively, fell into the selected criteria. These concerned 147 patients. Among these patients, 13 (8.8%) cases of drug-induced liver injuries were suspected. Seven cases were asymptomatic. Six cases were classified as serious by these criteria: hospitalization to investigate the cause of health status impairment (4 patients), prolongation of hospitalization (1 patient) and life-threathening (1 patient). Using the hospitalization database, the incidence of drug-induced liver injuries was estimated as 6.6 per 1000 inpatients a week. Only 1 case was reported by physicians in the same period. CONCLUSIONS: Computerization of biochemical data would allow the development of systems to improve detection of drug-induced injury. Moreover, underreporting remains important for such potentially serious ADRs, even in a university hospital. (+info)
Improving allergy alerting in a computerized physician order entry system.
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Computerized physician order entry has been shown to reduce the frequency of serious medication errors. Decision support tools such as alerting functions for patient medication allergy are a key part of these applications. However, optimal performance requires iterative refinement. As systems become increasingly complex, mechanisms to monitor their performance become increasingly critical. We analyzed trend data obtained over a five-year period that showed decreasing compliance to allergy alert functions within computerized order entry. Many medication-allergy pairs were being consistently overridden. Renewal policies affecting reordering narcotics also contributed heavily to this trend. Each factor revealed a system-wide trend that could result in suggestions for policy or software change. Monitoring trends such as these is very important to maintain software correctness and ensure user trust in alerting systems, so users remain responsive to computerized alerts. (+info)