Lisa A. Prosser

A cost-benefit analysis of electronic medical records in primary care

Electronic medical record systems improve the quality of patient care and decrease medical errors, but their financial effects have not been as well documented. The purpose of this study was to estimate the net financial benefit or cost of implementing electronic medical record systems in primary care. We performed a cost-benefit study to analyze the financial effects of electronic medical record systems in ambulatory primary care settings from the perspective of the health care organization. Data were obtained from studies at our institution and from the published literature. The reference strategy for comparisons was the traditional paper-based medical record. The primary outcome measure was the net financial benefit or cost per primary care physician for a 5-year period. The estimated net benefit from using an electronic medical record for a 5-year period was 86,400 US dollars per provider. Benefits accrue primarily from savings in drug expenditures, improved utilization of radiology tests, better capture of charges, and decreased billing errors. In one-way sensitivity analyses, the model was most sensitive to the proportion of patients whose care was capitated; the net benefit varied from a low of 8400 US dollars to a high of 140,100 US dollars . A five-way sensitivity analysis with the most pessimistic and optimistic assumptions showed results ranging from a 2300 US dollars net cost to a 330,900 US dollars net benefit. Implementation of an electronic medical record system in primary care can result in a positive financial return on investment to the health care organization. The magnitude of the return is sensitive to several key factors.

Conjoint analysis applications in health - A checklist: A report of the ISPOR Good Research Practices for Conjoint Analysis Task Force

BACKGROUND The application of conjoint analysis (including discrete-choice experiments and other multiattribute stated-preference methods) in health has increased rapidly over the past decade. A wider acceptance of these methods is limited by an absence of consensus-based methodological standards. OBJECTIVE The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Research Practices for Conjoint Analysis Task Force was established to identify good research practices for conjoint-analysis applications in health. METHODS The task force met regularly to identify the important steps in a conjoint analysis, to discuss good research practices for conjoint analysis, and to develop and refine the key criteria for identifying good research practices. ISPOR members contributed to this process through an extensive consultation process. A final consensus meeting was held to revise the article using these comments, and those of a number of international reviewers. RESULTS Task force findings are presented as a 10-item checklist covering: 1) research question; 2) attributes and levels; 3) construction of tasks; 4) experimental design; 5) preference elicitation; 6) instrument design; 7) data-collection plan; 8) statistical analyses; 9) results and conclusions; and 10) study presentation. A primary question relating to each of the 10 items is posed, and three sub-questions examine finer issues within items. CONCLUSIONS Although the checklist should not be interpreted as endorsing any specific methodological approach to conjoint analysis, it can facilitate future training activities and discussions of good research practices for the application of conjoint-analysis methods in health care studies.

Cost-Effectiveness of Cholesterol-Lowering Therapies according to Selected Patient Characteristics

Several large-scale, long-term clinical trials evaluating statin drugs (3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors) have confirmed the beneficial effect of reducing cholesterol levels on coronary event rates and related mortality (1-5). Statin drugs are expensive, especially considering the large number of persons who could potentially benefit from cholesterol-lowering therapies. As a result, many analyses have focused on the costs, resource use, and cost-effectiveness of using statins to lower cholesterol levels (6-15). In this analysis, the cost-effectiveness of primary and secondary prevention with cholesterol-lowering therapies was evaluated in separate risk subgroups to assess how cost-effectiveness varies with individual patient characteristics. This analysis extends the results of previous analyses by examining a greater number of specific patient subgroups, particularly with respect to primary prevention. It improves on previous analyses by including updated costs and epidemiologic estimates and by following recommendations from the U.S. Panel on Cost-Effectiveness in Health and Medicine. The incremental cost-effectiveness of statins compared with diet therapy was explicitly modeled. Finally, we compared the cost-effectiveness results with the treatment guidelines recommended by the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II) (16). Methods The analysis used a previously validated computer simulation model, the Coronary Heart Disease Policy Model (17-19), to estimate the effects and costs of each cholesterol-lowering strategy in each risk group. The assumptions and design of the Coronary Heart Disease Policy Model are described in detail elsewhere (17, 18). The model consists of three integrated submodels: the demographic-epidemiologic submodel, the bridge submodel, and the disease history submodel. The demographic-epidemiologic submodel predicts coronary heart disease incidence and noncoronary heart disease mortality among people 35 to 84 years of age without coronary heart disease. The risk function for incidence of coronary heart disease is based on age, sex, diastolic blood pressure, smoking status, low-density lipoprotein (LDL) cholesterol level, and high-density lipoprotein (HDL) cholesterol level. The risk function for noncoronary heart disease mortality includes age, sex, diastolic blood pressure, and smoking status. Noncoronary heart disease mortality is assumed to be unaffected by serum cholesterol levels (1, 2). After a person in the model develops coronary heart disease, he or she moves into the bridge submodel, which characterizes the initial coronary heart disease event (cardiac arrest, myocardial infarction, or angina) and the sequelae in the first 30 days after the event. The disease history submodel tracks the subsequent development of coronary heart disease events, revascularization procedures (coronary artery bypass grafting and angioplasty), coronary heart disease mortality, and noncoronary heart disease mortality among patients with coronary heart disease. Target Population The base-case analysis evaluated the cost-effectiveness of primary and secondary prevention in all persons with LDL cholesterol levels of 4.1 mmol/L or greater ( 160 mg/dL). Specific subgroup analyses examined how the cost-effectiveness changed according to patient age (35 to 44, 45 to 54, 55 to 64, 65 to 74, or 75 to 84 years), sex, smoking status (yes or no), diastolic blood pressure (<95 mm Hg or 95 mm Hg), HDL cholesterol level (<0.9, 0.9 to 1.3, or>1.3 mmol/L [<35, 35 to 49, or 50 mg/dL]), and LDL cholesterol level (4.2 to 4.9 or 4.9 mmol/L [160 to 189 or 190 mg/dL]). These risk factors closely correspond to but are not exactly the same as the National Cholesterol Education Program risk factor definitions (16). Effectiveness Lipid Levels Results from five studies were pooled to estimate the effects of a low-cholesterol diet (step I diet) on cholesterol levels (20-24). Data used to model the effectiveness of primary prevention with a statin came from three long-term studies of pravastatin, 40 mg/d, because the quality of effectiveness data was high for this dosage (2, 25, 26). Effectiveness estimates for secondary prevention with a statin was based on results from the Scandinavian Simvastatin Survival Study (Appendix Table 1) (1). A 2-year time lag between the start of treatment and the effects of treatment on coronary events was assumed (1, 2). Quality of Life Quality-of-life weights in the general population without coronary heart disease were based on data from the Beaver Dam Health Outcomes Study according to age and sex (27). Additional quality-of-life adjustments for coronary heart diseaserelated morbidity were made for persons in the disease history submodel; because community preferences were not available, these adjustments were based on a survey of Medicare patients with a history of coronary heart disease (28, 29). Costs In the Coronary Heart Disease Policy Model, total costs were calculated as the sum of intervention costs, costs of coronary heart disease care, and costs of noncoronary heart disease health care. All costs were converted to 1997 U.S. dollars by using the Medical Care Component of the Consumer Price Index. Intervention costs included the costs of medication, physician visits (including the associated patient time), and laboratory tests. National Cholesterol Education Program guidelines were used to guide estimates of the number of physician visits and laboratory tests each year (16). Medication Medication costs for primary and secondary prevention with statins were calculated by using the average wholesale prices of pravastatin and simvastatin, respectively (30). The base-case analysis does not include adjustment of future medication costs resulting from loss of patent protection for statin drugs. To adjust for compliance, it was assumed that patients receiving statins take 95% of the suggested regimen (31); this average compliance rate is reflected in the pool of studies from which the estimates of effectiveness were derived (Appendix Table 1). Primary Prevention Patients receiving diet therapy were assumed to have two physician visits per year. Patients receiving primary prevention with a statin were assumed to have five physician visits in the first year and two physician visits in each year after the first year. A cost of

Randomized controlled trial to improve primary care to prevent and manage childhood obesity the high five for kids study

34.34 was associated with each office visit (32, 33). In addition, the value of patient time associated with each visit was estimated by multiplying the average time per visit (including travel, waiting, and encounter times) by age- and sex-specific average hourly wages. These age- and sex-adjusted patient time costs range from approximately

Economic Burden of Childhood Autism Spectrum Disorders

12 to

Health Benefits, Risks, and Cost-Effectiveness of Influenza Vaccination of Children

26 per physician visit (34, 35). Patients receiving diet therapy were assumed to have one chemical profile, one HDL measurement, and one mid-year measurement of total cholesterol, for a total annual laboratory cost of

Addition of Choice of Complementary Therapies to Usual Care for Acute Low Back Pain: A Randomized Controlled Trial

39.49 (36). Patients receiving drug therapy were assumed to receive five sets of tests in the first year (five chemical profiles and five HDL measurements for a cost of

Economic and other barriers to adopting recommendations to prevent childhood obesity: results of a focus group study with parents

151.55) and two sets of tests in each subsequent year (

Non-Traditional Settings for Influenza Vaccination of Adults : Costs and Cost Effectiveness

60.62) (36). The resulting cost estimate of primary prevention with step I diet was

Estimated morbidity and mortality in adolescents and young adults diagnosed with Type 2 diabetes mellitus

108 per year. For primary prevention with a statin, cost estimates were