The Future of Family Medicine: a collaborative project of the family medicine community.
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BACKGROUND: Recognizing fundamental flaws in the fragmented US health care systems and the potential of an integrative, generalist approach, the leadership of 7 national family medicine organizations initiated the Future of Family Medicine (FFM) project in 2002. The goal of the project was to develop a strategy to transform and renew the discipline of family medicine to meet the needs of patients in a changing health care environment. METHODS: A national research study was conducted by independent research firms. Interviews and focus groups identified key issues for diverse constituencies, including patients, payers, residents, students, family physicians, and other clinicians. Subsequently, interviews were conducted with nationally representative samples of 9 key constituencies. Based in part on these data, 5 task forces addressed key issues to meet the project goal. A Project Leadership Committee synthesized the task force reports into the report presented here. RESULTS: The project identified core values, a New Model of practice, and a process for development, research, education, partnership, and change with great potential to transform the ability of family medicine to improve the health and health care of the nation. The proposed New Model of practice has the following characteristics: a patient-centered team approach; elimination of barriers to access; advanced information systems, including an electronic health record; redesigned, more functional offices; a focus on quality and outcomes; and enhanced practice finance. A unified communications strategy will be developed to promote the New Model of family medicine to multiple audiences. The study concluded that the discipline needs to oversee the training of family physicians who are committed to excellence, steeped in the core values of the discipline, competent to provide family medicine's basket of services within the New Model, and capable of adapting to varying patient needs and changing care technologies. Family medicine education must continue to include training in maternity care, the care of hospitalized patients, community and population health, and culturally effective and proficient care. A comprehensive lifelong learning program for each family physician will support continuous personal, professional, and clinical practice assessment and improvement. Ultimately, systemwide changes will be needed to ensure high-quality health care for all Americans. Such changes include taking steps to ensure that every American has a personal medical home, promoting the use and reporting of quality measures to improve performance and service, advocating that every American have health care coverage for basic services and protection against extraordinary health care costs, advancing research that supports the clinical decision making of family physicians and other primary care clinicians, and developing reimbursement models to sustain family medicine and primary care practices. CONCLUSIONS: The leadership of US family medicine organizations is committed to a transformative process. In partnership with others, this process has the potential to integrate health care to improve the health of all Americans. (+info)
Evaluating computer capabilities in a primary care practice-based research network.
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PURPOSE: We wanted to assess computer capabilities in a primary care practice-based research network and to understand how receptive the practices were to new ideas for automation of practice activities and research. METHOD: This study was conducted among members of the Pediatric Practice Research Group (PPRG). A survey to assess computer capabilities was developed to explore hardware types, software programs, Internet connectivity and data transmission; views on privacy and security; and receptivity to future electronic data collection approaches. RESULTS: Of the 40 PPRG practices participating in the study during the autumn of 2001, all used IBM-compatible systems. Of these, 45% used stand-alone desktops, 40% had networked desktops, and approximately 15% used laptops and minicomputers. A variety of software packages were used, with most practices (82%) having software for some aspect of patient care documentation, patient accounting (90%), business support (60%), and management reports and analysis (97%). The main obstacles to expanding use of computers in patient care were insufficient staff training (63%) and privacy concerns (82%). If provided with training and support, most practices indicated they were willing to consider an array of electronic data collection options for practice-based research activities. CONCLUSIONS: There is wide variability in hardware and software use in the pediatric practice setting. Implementing electronic data collection in the PPRG would require a substantial start-up effort and ongoing training and support at the practice site. (+info)
Census of clinics providing specialist lipid services in the United Kingdom.
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BACKGROUND: Screening for familial hypercholesterolaemia (FH) through family tracing of relatives is cost-effective, but access to the index patient through specialist lipid clinics is a determinant of the programme's success. This paper reports on numbers of FH patients and on specialist lipid clinic provision in the United Kingdom. RESULTS: One hundred and forty-four clinics provide specialist lipid services. Over 20 per cent of clinics do not employ a nurse and 64 per cent employ only one doctor. Two thirds treat children. Thirty-four clinics (24 per cent) have computerized records, 58 plan to and 66 clinics were unable to estimate FH numbers. Data from the responding clinics identified 4665 'definite' and 6024 'probable' FH cases. By extrapolation, we estimate there are 19 794 FH patients treated in specialist centres, 17 per cent of the predicted number. COMMENT: Specialist lipid clinic provision is patchy. Less than 10 per cent of the predicted FH patients in the UK are recorded on computerized databases limiting implementation of cascade testing. Substantial investment in the infrastructure of specialist lipid clinics is needed. (+info)
Functional characteristics of commercial ambulatory electronic prescribing systems: a field study.
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OBJECTIVE: To compare the functional capabilities being offered by commercial ambulatory electronic prescribing systems with a set of expert panel recommendations. DESIGN: A descriptive field study of ten commercially available ambulatory electronic prescribing systems, each of which had established a significant market presence. Data were collected from vendors by telephone interview and at sites where the systems were functioning through direct observation of the systems and through personal interviews with prescribers and technical staff. MEASUREMENTS: The capabilities of electronic prescribing systems were compared with 60 expert panel recommendations for capabilities that would improve patient safety, health outcomes, or patients' costs. Each recommended capability was judged as having been implemented fully, partially, or not at all by each system to which the recommendation applied. Vendors' claims about capabilities were compared with the capabilities found in the site visits. RESULTS: On average, the systems fully implemented 50% of the recommended capabilities, with individual systems ranging from 26% to 64% implementation. Only 15% of the recommended capabilities were not implemented by any system. Prescribing systems that were part of electronic health records (EHRs) tended to implement more recommendations. Vendors' claims about their systems' capabilities had a 96% sensitivity and a 72% specificity when site visit findings were considered the gold standard. CONCLUSIONS: The commercial electronic prescribing marketplace may not be selecting for capabilities that would most benefit patients. Electronic prescribing standards should include minimal functional capabilities, and certification of adherence to standards may need to take place where systems are installed and operating. (+info)
Twelve-month drug cost savings related to use of an electronic prescribing system with integrated decision support in primary care.
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OBJECTIVE: We reported previously the results of a 6-month controlled trial in which the use of a commercially available electronic prescribing system with integrated clinical decision support and evidence-based message capability was associated with significantly lower primary care drug costs. The original study focused on new prescriptions, defined as claims for a medication that the patient had not received in the previous 12 months. The main objectives of this follow-up report were to (a) determine if the 6-month savings on new prescriptions were sustained during 12 months of follow-up, (b) evaluate the impact of the computerized decision support system (CDSS) on all pharmacy claims and per-member-per-month (PMPM) expenditures, and (c) evaluate the prescribing behaviors within 8 high-cost therapeutic categories that were frequently targeted by the electronic messages to prescribers to help verify that the drug cost savings were due to the recommendations in the electronic prescribing system. METHODS: Two database queries were performed to identify additional pharmacy claims data for all Network Health Plan patients who were cared for by the 38 primary care clinicians (32 physicians, 4 nurse practitioners, and 2 physician assistants) included in our original 6-month study. This follow-up analysis (a) identified all new prescription claims for the 2 groups of clinicians throughout the 12-month follow-up period (June 2002 through May 2003) and (b) assessed all pharmacy claims during the same 12-month period to provide more complete savings estimates and to examine between-group differences in PMPM expenditures. RESULTS: During 12 months of follow-up, clinicians using the electronic prescribing system continued to have lower prescription costs than the controls. Clinicians using the electronic prescribing system had average costs for 26,674 new prescriptions that were dollar 4.12 lower (95% confidence interval, dollar 1.53-dollar 6.71; P=0.003) and PMPM expenditures that were dollar 0.57 lower than expected based on the changes observed for 24,507 new prescriptions written by clinicians in the control group. The average drug cost savings on new prescriptions were dollar 482 per prescriber per month (PPPM), based upon prescription cost and dollar 465 PPPM based upon PMPM analysis. When all pharmacy claims (156,429) were analyzed, the intervention group.s average prescription cost was dollar 2.57 lower and their PMPM expenditures were dollar 1.07 lower than expected based on the changes observed in the control group. The average drug cost savings on all pharmacy claims were dollar 863 PPPM based on average prescription cost and dollar 873 PPPM based on PMPM analysis. The proportion of prescriptions for highcost drugs that were the target of the CDSS messages to prescribers was a relative 17.5% lower among the intervention group (35.8%) compared with the control group (43.4%; P=0.03). CONCLUSIONS: An electronic prescribing system with integrated decision support shifted prescribing behavior away from high-cost therapies and significantly lowered prescription drug costs. The savings associated with altered prescribing behavior offset the monthly subscription cost of the system. (+info)
Effects of electronic prescribing on formulary compliance and generic drug utilization in the ambulatory care setting: a retrospective analysis of administrative claims data.
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OBJECTIVE: Electronic prescribing (e-prescribing) provides formulary information at the point of care. The objective of this study was to assess the effects of e-prescribing on formulary compliance and generic utilization. METHODS: This was a retrospective analysis of pharmacy claims data from a large national managed care organization. A sample of 95 providers using predominantly e-prescribing was randomly selected (e-prescriber group). A matched sample of 95 traditional prescribers was selected (traditional prescriber group), matched to the e-prescriber group by zip code and medical specialty. A total of 110,975 paid pharmacy claims, for the 12 months from August 1, 2001, through July 31, 2002, were analyzed to assess the effect of e-prescribing on formulary compliance and generic utilization. All paid pharmacy claims were examined for each group; for the e-prescriber group, this included all claims, not just those prescribed using an e-prescribing device. A written qualitative survey was distributed to physicians and office managers to assess e-prescribing usage, sources of formulary information, and effects of e-prescribing on office resources. RESULTS: Both predominantly e-prescribers and traditional prescribers demonstrated high levels of formulary compliance, 83.2% versus 82.8%, respectively (P=0.32). Formulary compliance for these groups did not differ from the overall prescriber population (82.0%). There was not a difference in generic drug utilization rates between e-prescribers and traditional prescribers (absolute rates 37.3% versus 36.9%, P=0.18). Qualitative survey responses supported previously reported research indicating reductions in calls both to and from pharmacies for prescription orders. CONCLUSIONS: An examination of paid pharmacy claims from a large, national managed care organization demonstrated no differences between predominantly e-prescribers and traditional prescribers in measures of formulary compliance or generic drug utilization. Future studies should examine keystroke data at the point of care to observe more detail about drug selection methods. (+info)
Outpatient prescribing errors and the impact of computerized prescribing.
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BACKGROUND: Medication errors are common among inpatients and many are preventable with computerized prescribing. Relatively little is known about outpatient prescribing errors or the impact of computerized prescribing in this setting. OBJECTIVE: To assess the rates, types, and severity of outpatient prescribing errors and understand the potential impact of computerized prescribing. DESIGN: Prospective cohort study in 4 adult primary care practices in Boston using prescription review, patient survey, and chart review to identify medication errors, potential adverse drug events (ADEs) and preventable ADEs. PARTICIPANTS: Outpatients over age 18 who received a prescription from 24 participating physicians. RESULTS: We screened 1879 prescriptions from 1202 patients, and completed 661 surveys (response rate 55%). Of the prescriptions, 143 (7.6%; 95% confidence interval (CI) 6.4% to 8.8%) contained a prescribing error. Three errors led to preventable ADEs and 62 (43%; 3% of all prescriptions) had potential for patient injury (potential ADEs); 1 was potentially life-threatening (2%) and 15 were serious (24%). Errors in frequency (n=77, 54%) and dose (n=26, 18%) were common. The rates of medication errors and potential ADEs were not significantly different at basic computerized prescribing sites (4.3% vs 11.0%, P=.31; 2.6% vs 4.0%, P=.16) compared to handwritten sites. Advanced checks (including dose and frequency checking) could have prevented 95% of potential ADEs. CONCLUSIONS: Prescribing errors occurred in 7.6% of outpatient prescriptions and many could have harmed patients. Basic computerized prescribing systems may not be adequate to reduce errors. More advanced systems with dose and frequency checking are likely needed to prevent potentially harmful errors. (+info)
Automated identification of a physician's primary patients.
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OBJECTIVE: To develop and validate an automated method for determining the set of patients for whom a given primary care physician holds overall clinical responsibility. DESIGN: The study included all adult patients (16,185) seen at least once in an ambulatory setting during a three-year period by 18 primary care physicians in ten practices. The physicians indicated whether they considered themselves to be the physician primarily responsible for the overall clinical care of each visiting patient. Statistical models were constructed to predict the physicians' designations using predictor variables derived from electronically available appointment schedules and demographic information. MEASUREMENTS: Predictive accuracy was assessed primarily using the area under the receiver-operating characteristic curve (AUC), and secondarily using positive predictive value (PPV) and sensitivity. RESULTS: A minimal set of six variables was identified as predictive of the physicians' designations. The constructed model had a median AUC for individual physicians of 0.92 (interquartile interval: 0.90-0.96), a PPV of 0.94 (interquartile interval: 0.87-0.95), and a sensitivity of 0.95 (interquartile interval: 0.87-0.97). CONCLUSION: A statistical model using a minimal set of commonly available electronic data can accurately predict the set of patients for whom a physician holds primary clinical responsibility. Further research examining the generalization of the model to other settings would be valuable. (+info)