Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership.

BACKGROUND: Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs. OBJECTIVES: In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM. METHODS: The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules. RESULTS: Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system's medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM. CONCLUSIONS: The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.

Health-risk-assessment tools used to predict costs in defined populations.

With the Balanced Budget Act of 1997 mandating that the Health Care Financing Administration (HCFA) implement risk-adjusted payment mechanisms for Medicare managed care plans (Medicare + Choice) by January 2000, risk-adjustment tools will play an important role in future capitated reimbursement. This is because there is growing evidence that healthier-than-average beneficiaries select Medicare + Choice. The risk adjustment that HCFA has adopted is initially based on primary inpatient diagnosis from hospitalizations in the previous year. Other payers are likely to adopt similar payment mechanisms. This article reviews nineteen risk-adjustment research papers, including the tool adopted for Medicare + Choice, some of which are likely to form the basis for subsequent HCFA risk-adjustment methods. In general, claims-based models are more powerful in predicting total costs than survey-based or demographics-based models. Survey-based models, although expensive and not as powerful claims-based models, can be used when claims data are unavailable. One of the most popular survey-based tools, SF-36, is likely to become increasingly important because HCFA will be using it to measure quality outcomes from Medicare + Choice plans and will make the results public. All of the models reviewed have limitations, but can be expected to be building blocks for future risk-based capitated reimbursement.

Improving organizational processes for gains during implementation.

Reengineering work processes has not been a traditional part of health care automation. With the new emphasis on quality and cost cutting, users are more receptive to improving processes as they are automated in new or replacement systems. The authors describe a process flowcharting technique, which, as part of the implementation design phase, assists users in understanding and engineering the processes impacted during the implementation. Also described is a strategy for securing cooperation from physicians and other key players.