Home blood pressure monitoring, secure electronic messaging and medication intensification for improving hypertension control: a mediation analysis.

OBJECTIVE: We evaluated the role of home monitoring, communication with pharmacists, medication intensification, medication adherence and lifestyle factors in contributing to the effectiveness of an intervention to improve blood pressure control in patients with uncontrolled essential hypertension. METHODS: We performed a mediation analysis of a published randomized trial based on the Chronic Care Model delivered over a secure patient website from June 2005 to December 2007. Study arms analyzed included usual care with a home blood pressure monitor and usual care with home blood pressure monitor and web-based pharmacist care. Mediator measures included secure messaging and telephone encounters; home blood pressure monitoring; medications intensification and adherence and lifestyle factors. Overall fidelity to the Chronic Care Model was assessed with the Patient Assessment of Chronic Care (PACIC) instrument. The primary outcome was percent of participants with blood pressure (BP) <140/90 mm Hg. RESULTS: At 12 months follow-up, patients in the web-based pharmacist care group were more likely to have BP <140/90 mm Hg (55%) compared to patients in the group with home blood pressure monitors only (37%) (p = 0.001). Home blood pressure monitoring accounted for 30.3% of the intervention effect, secure electronic messaging accounted for 96%, and medication intensification for 29.3%. Medication adherence and self-report of fruit and vegetable intake and weight change were not different between the two study groups. The PACIC score accounted for 22.0 % of the main intervention effect. CONCLUSIONS: The effect of web-based pharmacist care on improved blood pressure control was explained in part through a combination of home blood pressure monitoring, secure messaging, and antihypertensive medication intensification.

Health care utilization and costs by metabolic syndrome risk factors.

BACKGROUND: This study compared prevalent health utilization and costs for persons with and without metabolic syndrome and investigated the independent associations of the various factors that make up metabolic syndrome. METHODS: Subjects were enrollees of three health plans who had all clinical measurements (blood pressure, fasting plasma glucose, body mass index, triglycerides, and high-density lipoprotein cholesterol) necessary to determine metabolic syndrome risk factors over the 2-year study period (n = 170,648). We used clinical values, International Classification of Diseases, Ninth Revision (ICD-9) diagnoses, and medication dispensings to identify risk factors. We report unadjusted mean annual utilization and modeled mean annual costs adjusting for age, sex, and co-morbidity. RESULTS: Subjects with metabolic syndrome (n = 98,091) had higher utilization and costs compared to subjects with no metabolic syndrome (n = 72,557) overall, and when stratified by diabetes (P < 0.001). Average annual total costs between subjects with metabolic syndrome versus no metabolic syndrome differed by a magnitude of 1.6 overall ($5,732 vs. $3,581), and a magnitude of 1.3 when stratified by diabetes (diabetes, $7,896 vs. $6,038; no diabetes, $4,476 vs. $3,422). Overall, total costs increased by an average of 24% per additional risk factor (P < 0.001). Costs and utilization differed by risk factor clusters, but the more prevalent clusters were not necessarily the most costly. Costs for subjects with diabetes plus weight risk, dyslipidemia, and hypertension were almost double the costs for subjects with prediabetes plus similar risk factors ($8,067 vs. $4,638). CONCLUSIONS: Metabolic syndrome, number of risk factors, and specific combinations of risk factors are markers for high utilization and costs among patients receiving medical care. Diabetes and certain risk clusters are major drivers of utilization and costs.

Development and estimation of a pediatric chronic disease score using automated pharmacy data.

BACKGROUND: Although risk assessment models for specific adult populations such as the elderly have been developed, little work has focused on developing pediatric-specific models. The lack of pediatric models may result in incorrect estimates of relative disease severity among children, in reduced reimbursement for health plans and providers, and in inadequate health care for chronically ill children. OBJECTIVES: To develop and to evaluate a pediatric risk assessment model using automated pharmacy data. DESIGN: Retrospective, case-cohort study using automated data. SUBJECTS: All children continuously enrolled in Group Health Cooperative of Puget Sound during 1992 and 1993. MEASURES: The Pediatric Chronic Disease Score (PCDS), an algorithm that classified children into chronic disease categories by prescription drug fills, was compared with the ICD-9-CM-based Ambulatory Care Groups (ACG) model and a demographic model for prediction of total, ambulatory, or primary care costs and primary care visits. Forecast models were estimated using linear regression and they were evaluated with R2, mean prediction error, mean squared prediction error, and Mincer-Zarnowitz tests. RESULTS: The pharmacy-based PCDS performed significantly better on each of the four forecasting accuracy tests than did a demographic model (eg, R2s averaging fourfold higher). Compared with the ACG model, the PCDS model performed similarly on mean squared prediction error tests; however, the ACG generally had higher validation R2 values. CONCLUSIONS: A pharmacy-based pediatric risk assessment model performs better than a demographic model and represents a viable alternative to ICD-9-CM-based models. Further research is necessary to determine if children must be considered separately from adults when conducting population-based risk assessments.

The sensitivity and specificity of forecasting high-cost users of medical care.

OBJECTIVES: This study compares the ability of 3 risk-assessment models to distinguish high and low expense-risk status within a managed care population. Models are the Global Risk-Assessment Model (GRAM) developed at the Kaiser Permanente Center for Health Research; a logistic version of GRAM; and a prior-expense model. GRAM was originally developed for use in adjusting Medicare payments to health plans. METHODS: Our sample of 98,985 cases was drawn from random samples of memberships of 3 staff/group health plans. Risk factor data were from 1992 and expenses were measured for 1993. Models produced distributions of individual-level annual expense forecasts (or predicted probabilities of high expense-risk status for logistic) for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution to analyze the models' ability to distinguish high and low expense-risk status. Forecast stability was analyzed through bootstrapping. RESULTS: GRAM discriminates better overall than its comparators (although the models are similar for policy-relevant thresholds). All models forecast the highest-cost cases relatively well. GRAM forecasts high expense-risk status better than its comparators within chronic and serious disease categories that are amenable to early intervention but also generates relatively more false positives within these categories. CONCLUSIONS: This study demonstrates the potential of risk-assessment models to inform care management decisions by efficiently screening managed care populations for high expense-risk. Such models can act as preliminary screens for plans that can refine model forecasts with detailed surveys. Future research should involve multiple-year data sets to explore the temporal stability of forecasts.

Health-based payment and computerized patient record systems.

Health care information technology is changing rapidly and dramatically. A small but growing number of clinicians, especially those in staff and group model HMOs and hospital-affiliated practices, are automating their patient medical records in response to pressure to improve quality and reduce costs. Computerized patient record systems in HMOs track risks, diagnoses, patterns of care, and outcomes across large populations. These systems provide access to large amounts of clinical information; as a result, they are very useful for risk-adjusted or health-based payment. The next stage of evolution in health-based payment is to switch from fee-for-service (claims) to HMO technology in calculating risk coefficients. This will occur when HMOs accumulate data sets containing records on provider-defined disease episodes, with every service linked to its appropriate disease episode for millions of patients. Computerized patient record systems support clinically meaningful risk-assessment models and protect patients and medical groups from the effects of adverse selection. They also offer significant potential for improving quality of care.

Managed care data and public health: the experience of Group Health Cooperative of Puget Sound.

Traditional group and staff model HMOs have contributed to public health investigations for decades. HMOs offer several advantages for this type of research because of their defined population and provider groups and the integrated nature of care delivery in these organizations. Traditional HMOs have also made investments in sophisticated data systems to support evidence-based care that is supported by high-quality clinical data available in automated information systems. This paper reviews why traditional HMOs are ideal places to conduct public health research and analyzes how recent market trends may threaten this role for managed care.

Building health plan databases to risk adjust outcomes and payments.

OBJECTIVES: To highlight the types and sources of data on medical risk and outcomes routinely collected by managed care organizations over time; to summarize the quality and consistency of these data; and to describe some of the difficulties that arise in collecting, pooling, and using these data. DESIGN: Synthesis of the experiences of two risk-adjustment modeling projects in assembling large volumes of demographic, diagnostic, and expense data from several health maintenance organizations (HMOs) over multiple years. SETTING: Six large HMOs from the Northwest, North Central, and Northeast regions of the USA. INTERVENTIONS: Health plans were approached to participate in a risk-adjustment study, presented with an extensive variable-by-variable data request, and, if willing to participate, asked to specify a desired process for extracting, copying, and transferring selected variables to the study site for purposes of research. Depending on local circumstances, three different approaches were used: (i) health plan staff obtained the data and organized them into the requested study format; (ii) study staff were provided access to health plan data systems to perform the extractions directly; and (iii) health plans hired contract programmers to perform the extractions under the direction of the study team. Key measures of risk and cost were extracted and merged into analysis files. MAIN OUTCOME MEASURES: Complete and consistent eligibility maps, demographic information, inpatient and outpatient diagnoses, and total health plan expense for each enrollee. RESULTS: We have been successful in collecting and integrating complete utilization, morbidity, demographic, and cost data on total memberships of five large HMOs as well as a subset from a sixth HMO, all for multiple years. CONCLUSION: While HMOs vary greatly in the quality and comprehensiveness of their data systems, these attributes have been improving across the board over time. Automated health plan data systems represent potentially valuable sources of data on health risks and outcomes and can be used to benchmark disease management programs and risk adjust capitation payments and medical outcomes.

The triad of pneumonitis, pleuritis, and pericarditis in juvenile rheumatoid arthritis.

There are few reports of pleuroparenchymal and pericardial involvement secondary to juvenile rheumatoid arthritis (JRA) in the literature. This article presents two such cases and gathers scattered information about this combination previously reported by few others. We wish to emphasize that the combination of pericardial and pleuropulmonary involvement in a youngster may be the only presenting clinical manifestations of rheumatoid arthritis, whereas in others, it may be associated with arthritic symptoms at the same time.