The impact of the electronic medical record on structure,process, and outcomes within primary care: a systematic review of the evidence

Background

The electronic medical record (EMR)/electronic health record (EHR) is becoming an integral component of many primary-care outpatient practices. Before implementing an EMR/EHR system, primary-care practices should have an understanding of the potential benefits and limitations.

Objective

The objective of this study was to systematically review the recent literature around the impact of the EMR/EHR within primary-care outpatient practices.

Materials and methods

Searches of Medline, EMBASE, CINAHL, ABI Inform, and Cochrane Library were conducted to identify articles published between January 1998 and January 2010. The gray literature and reference lists of included articles were also searched. 30 studies met inclusion criteria.

Results and discussion

The EMR/EHR appears to have structural and process benefits, but the impact on clinical outcomes is less clear. Using Donabedian''s framework, five articles focused on the impact on healthcare structure, 21 explored healthcare process issues, and four focused on health-related outcomes.     

Care transitions as opportunities for clinicians to use data exchange services: how often do they occur?

Background

The electronic exchange of health information among healthcare providers has the potential to produce enormous clinical benefits and financial savings, although realizing that potential will be challenging. The American Recovery and Reinvestment Act of 2009 will reward providers for ‘meaningful use’ of electronic health records, including participation in clinical data exchange, but the best ways to do so remain uncertain.

Methods

We analyzed patient visits in one community in which a high proportion of providers were using an electronic health record and participating in data exchange. Using claims data from one large private payer for individuals under age 65 years, we computed the number of visits to a provider which involved transitions in care from other providers as a percentage of total visits. We calculated this ‘transition percentage’ for individual providers and medical groups.

Results

On average, excluding radiology and pathology, approximately 51% of visits involved care transitions between individual providers in the community and 36%–41% involved transitions between medical groups. There was substantial variation in transition percentage across medical specialties, within specialties and across medical groups. Specialists tended to have higher transition percentages and smaller ranges within specialty than primary care physicians, who ranged from 32% to 95% (including transitions involving radiology and pathology). The transition percentages of pediatric practices were similar to those of adult primary care, except that many transitions occurred among pediatric physicians within a single medical group.

Conclusions

Care transition patterns differed substantially by type of practice and should be considered in designing incentives to foster providers'' meaningful use of health data exchange services.     

Rank-based spatial clustering: an algorithm for rapid outbreak detection

Objective

Public health surveillance requires outbreak detection algorithms with computational efficiency sufficient to handle the increasing volume of disease surveillance data. In response to this need, the authors propose a spatial clustering algorithm, rank-based spatial clustering (RSC), that detects rapidly infectious but non-contagious disease outbreaks.

Design

The authors compared the outbreak-detection performance of RSC with that of three well established algorithms—the wavelet anomaly detector (WAD), the spatial scan statistic (KSS), and the Bayesian spatial scan statistic (BSS)—using real disease surveillance data on to which they superimposed simulated disease outbreaks.

Measurements

The following outbreak-detection performance metrics were measured: receiver operating characteristic curve, activity monitoring operating curve curve, cluster positive predictive value, cluster sensitivity, and algorithm run time.

Results

RSC was computationally efficient. It outperformed the other two spatial algorithms in terms of detection timeliness, and outbreak localization. RSC also had overall better timeliness than the time-series algorithm WAD at low false alarm rates.

Conclusion

RSC is an ideal algorithm for analyzing large datasets when the application of other spatial algorithms is not practical. It also allows timely investigation for public health practitioners by providing early detection and well-localized outbreak clusters.