The impact of electronic health records on ambulatory costs among Medicaid beneficiaries

Electronic medical records and primary care quality: Evidence from Manitoba

Improvements in quality of care through supporting decision-making processes and increased efficiency have prompted widespread implementation of electronic medical records (EMRs) in Canada. Using a set of indicators of preventive care, chronic disease management, and hospitalizations due to ambulatory care sensitive conditions (ACSC), this study measures the effect of EMR adoption on quality of primary care measures. Population-based data for the Canadian province of Manitoba are used in a difference-in-differences approach with patient- and time-fixed effects. Evidence of changes in the selected quality-of-care indicators is weak, with preventive care, management of asthma, and hospitalizations showing no significant change due to EMR adoption. A statistically significant increase in the quality of diabetes care was found for EMR users, changes being larger for late EMR adopters which is possibly explained by a network effect. This research demonstrates that measuring whether EMRs prompt changes in the quality of care confronts serious challenges. The rapid evolution and gradual adoption of EMR technology, the inevitable learning/acceptance process by individual health practitioners, and its potential reflection on different patient populations create unmeasurable variables that confound EMRs' impact. This study also underscores the importance of data development to support the economic value of EMRs.

Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions

OBJECTIVE

Development of systematic approaches for understanding and assessing data quality is becoming increasingly important as the volume and utilization of health data steadily increases. In this study, a taxonomy of data defects was developed and utilized when automatically detecting defects to assess Medicaid data quality maintained by one of the states in the United States.

MATERIALS AND METHODS

There were more than 2.23 million rows and 32 million cells in the Medicaid data examined. The taxonomy was developed through document review, descriptive data analysis, and literature review. A software program was created to automatically detect defects by using a set of constraints whose development was facilitated by the taxonomy.

RESULTS

Five major categories and seventeen subcategories of defects were identified. The major categories are missingness, incorrectness, syntax violation, semantic violation, and duplicity. More than 3 million defects were detected indicating substantial problems with data quality. Defect density exceeded 10% in five tables. The majority of the data defects belonged to format mismatch, invalid code, dependency-contract violation, and implausible value types. Such contextual knowledge can support prioritized quality improvement initiatives for the Medicaid data studied.

CONCLUSIONS

This research took the initial steps to understand the types of data defects and detect defects in large healthcare datasets. The results generally suggest that healthcare organizations can potentially benefit from focusing on data quality improvement. For those purposes, the taxonomy developed and the approach followed in this study can be adopted.

Electronic health record "super-users" and "under-users" in ambulatory care practices

OBJECTIVES

This study explored variation in the extent of use of electronic health record (EHR)-based health information technology (IT) functionalities across US ambulatory care practices. Use of health IT functionalities in ambulatory care is important for delivering high-quality care, including that provided in coordination with multiple practitioners.

STUDY DESIGN

We used data from the 2014 Healthcare Information and Management Systems Society Analytics survey. The responses of 30,123 ambulatory practices with an operational EHR were analyzed to examine the extent of use of EHR-based health IT functionalities for each practice.

METHODS

We created a novel framework for classifying ambulatory care practices employing 7 domains of health IT functionality. Drawing from the survey responses, we created a composite "use" variable indicating the extent of health IT functionality use across these domains. "Super-user" practices were defined as having near-full employment of the 7 domains of health IT functionalities and "under-users" as those with minimal or no use of health IT functionalities. We used multivariable logistic regression to investigate how the odds of super-use and under-use varied by practice size, type, urban or rural location, and geographic region.

RESULTS

Seventy-three percent of practices were not using EHR technologies to their full capability, and nearly 40% were classified as under-users. Under-user practices were more likely to be of smaller size, situated in the West, and located outside a metropolitan area.

CONCLUSIONS

To achieve the broader benefits of the EHR and health IT, health systems and policy makers need to identify and address barriers to full use of health IT functionalities.

Health Information Exchange Readiness for Demonstrating Return on Investment and Quality of Care

OBJECTIVE

To study the extent to which community health information exchanges (HIEs) deliver and measure return on investment (ROI) and improvements in the quality of care.

MATERIALS AND METHODS

We surveyed operational HIEs for their characteristics, information domains, impact on quality of care, and ROI.

RESULTS

A 60 percent response rate was achieved. Two-thirds of respondents agreed that community HIEs demonstrated a positive ROI, while one-third had no opinion or disagreed. One-fourth or fewer respondents reported using various metrics to calculate ROI. Most respondents agreed that HIEs improve the quality of care, though several were not sure and were awaiting further evidence. Most respondents indicated that they did not deliver reports on quality measures (76 percent) and that data were not being used to measure quality performance of participating providers (73 percent).

DISCUSSION

Respondents from most HIEs believe that the HIEs are demonstrating a positive ROI; however, a minority of them indicated they had used or will use specific metrics to calculate ROI. HIE representatives overwhelmingly reported that they believe the HIE activities improve the quality of healthcare delivered, but only a few are using data to evaluate provider performance or generate reports on quality measures.

CONCLUSION

This study demonstrates the challenge faced by policy makers and healthcare organizations that are investing millions of dollars in HIEs that are believed to improve health outcomes and increase efficiency, but still need more time to develop the evidence to confirm that belief. Our study shows that calculating ROI for HIEs or their impact on quality of care remains a secondary priority for most HIEs. This finding raises serious questions for the sustained support of HIEs, both financially and as a policy lever, given the end of Health Information Technology for Economic and Clinical Health (HITECH) Act funding.

Turning evidence into practice under payment reform: the new frontier of translational science

Momentum is building to replace the current fee-for-service payment system with value-based reimbursement models that aim to deliver high quality care at lower costs. Although the goals of payment and delivery system reforms to improve quality and reduce costs are clear, the actual path by which provider groups can achieve these goals is not well understood, in large part because the role of identifying and discouraging the use of low-value, high-cost services and encouraging the use of high-value, low-cost services has traditionally fallen to health plans, not provider groups. The shifting focus towards provider accountability for costs and quality promises to expand the role of provider organizations from mainly delivering care to both delivering and prioritizing it based on costs and quality. We discuss how progress on two important but challenging fronts will be needed for provider groups to successfully translate evidence into value. First, robust evidence on the costs and benefits of treatments will need to be developed and made easily accessible to provider groups. Second, provider groups will need to translate that evidence into systems that support cost-effective clinical decisions.