EHR (electronic health record) vs. EMR (electronic medical record)
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Request Demo. Populated by JS. Visit website. An electronic health records solution for practices of any size Watch a demo of our large practice and enterprise solution to see a fully integrated, scalable EHR system that helps you achieve interoperability, patient engagement, regulatory compliance, and value-based care delivery. Watch now. Features of our EHR that let you focus on what matters most. Quality reporting Automate registry reporting to easily meet regulatory requirements and commercial quality initiatives. Scalability Scale our clinical and electronic health records software solutions to support your organization, whether you are a solo practitioner or a large multispecialty group practice with complex billing needs.
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Benefit highlights of electronic health records from NextGen Healthcare. Value-based success Our electronic health records software technology meets industry standards and features clinical content, interoperability, and population health capabilities you need to achieve practice transformation for PCMH or successfully participate in alternative payment models APMs. Enhanced interoperability Our EHR solutions enable and ease data exchange with patients, providers, payers, health registries, and other organizations.
Streamlined charting Our nimble, intuitive clinical solutions allow you to chart within our electronic health records software quickly and easily, so you can focus on caring for your patients.
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Patient engagement Our solutions help improve patient engagement, keeping them coming back. How much time are you losing with your existing EHR software system? How many patients do you see per day? Linear regression models with prevalence as an outcome and regressors for ordinal age group 1: 18—34, 2: 35—49, 3: 50—64 and 4: 65—85 , dummy variables for system, and an age group-by-system interaction term were generated.
Separate models were fitted for the total population and for both sexes. The dataset received from Database A contained , encounters Fig. These data were cleaned and aggregated to patient unique-day resulting in , observations. Database B contained 2,, encounters; cleaning and aggregating resulted in , observations.
After matching Database A and B, there were , unique patient encounters by 72, patients. In all, These matched patients were Half Concordance of data between Database A and Database B was The most recent A1C test result values was concordant for Approximately half Discordant values for data missing in one system and not the other was Concordance of race was not assessed because Database B was missing race data for Sensitivity and positive predictive value were When comparing blood pressure readings alone, without diagnostic codes, for classification of hypertension which only captures persons with uncontrolled hypertension , sensitivity and positive predictive value were However, the slope of age-stratified prevalence across age groups did not differ between systems for either condition.
When stratified by both sex and age, the slope of prevalence did differ for males for both conditions. This study found important quality gaps in the use of clinical data for surveillance of diabetes and hypertension at a population level; nonetheless, high concordance of structured data demonstrate promise in an HIEs capacity to adequately capture data. In addition, while prevalence of disease was not the same in both health systems, this difference was consistent across age groups as demonstrated by parallel slopes of prevalence over age groups. Nonetheless, this analysis reveals several gaps in data reliability, especially for hypertension.
Only half of blood pressure readings were concordant between the two systems. High discordance of values for blood pressure readings, primarily from discordancy of missing values, resulted in high misclassification of hypertension. Sensitivity and positive predictive values based on blood pressure readings alone were only A possible explanation for this discordance is the way in which the mid-level health system sends data to the HIE.
Data is sent using unstructured transcript notes, which the HIE reads using natural language processing, but the health system sends structured data to its own data warehouse. Other studies have reported similar concerns with concordance between EHR systems and HIE data warehouses [ 15 , 16 ]. Data integrity might improve by requiring health systems to send patient data in structured formats to the HIE, such as through CCD [ 17 ].
The inability for the HIE to consistently capture important demographic information e. Reliable data collection on race, ethnicity, and language by EHRs is difficult [ 18 ]. High rates of misclassification and missing information have been documented across studies [ 19 , 20 ], even in settings with regulations promoting collection of these data [ 21 ].
This limitation could be mitigated by requiring health systems to capture and report structured codes for race and similar demographic and socioeconomic characteristics to the HIE. Some limitations exist in this analysis. First, the prevalence estimates of diabetes and hypertension are specific to the population studied i. The results presented in this analysis are not representative of the entire Utah healthcare seeking population nor do they capture non-health seeking populations.
Similarly, only a single health system was analyzed, and we do not know if these issues persist across health systems. In order to fully understand the utility of the HIE for surveillance, additional analysis on other health system that share data with the HIE would be necessary. Secondly, disease classification based on 1 year of healthcare encounters will fail to capture persons with disease who had just one encounter for an unrelated health event.
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For example, a patient having high blood pressure in a single visit will not have been classified as having hypertension, even if they may have had two high blood pressure readings the previous year. Though this prevents over estimation of hypertension from when blood pressure temporarily increases with illness, it might underestimate hypertension in persons with only a single health encounter.
Lastly, our analysis does not differentiate between data quality problems resulting from data entry errors and data transformation errors.
In conclusion, this study found that the Utah HIE is capable of providing useful, although limited, information for surveillance of diabetes and hypertension. Given its potential, a greater understanding is needed of the mechanisms by which HIEs capture, process, and store EHR data from multiple health systems, and how these processes affect measures of diabetes and hypertension. Public health agencies in places with fragmented healthcare and EHR systems, like Utah, might consider working with HIEs to address data quality issues, such as by mandating use of structured data fields, so that EHR data can be harnessed for population level chronic disease surveillance.
The data that support the findings of this study are available from the Utah Health Information Network but restrictions apply to the availability of these data, which were used under agreement for the current study, and so are not publicly available.
Monitoring and surveillance of chronic non-communicable diseases: progress and capacity in high-burden countries. A systematic review of publications assessing reliability and validity of the behavioral risk factor surveillance system BRFSS , — World Health Organization. Global Health diffusion of eHealth: making universal health coverage achievable.
Geneva: World Health Organization; Jamoom E, Yang N. Hyattsville; Using health information exchange to improve public health. Am J Public Health. Birkhead GS. Successes and continued challenges of electronic health Records for Chronic Disease Surveillance.
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Accessed 23 May Health Insurance Portability and Accountability Act of American Diabetes Association. Classification and Diagnosis of Diabetes. Diabetes Care.
NIH Publication No. R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; Dowle M, Srinivasan A. Lumley T. Analysis of complex survey samples. J Stat Softw. Validating health information exchange HIE data for quality measurement across four hospitals. How the continuity of care document can advance medical research and public health. Obtaining data on patient race, ethnicity, and primary language in health care organizations: current challenges and proposed solutions.
Health Serv Res. Med Care. Accuracy of race, ethnicity, and language preference in an electronic health record. J Gen Intern Med. Download references. The authors would like to acknowledge the invaluable contributions made by persons from the mid-level health system who assisted in various phases of the study from pulling data in their system warehouse to interpreting analysis results.
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This work would not have been possible without their close collaboration. We would also like to thank the Utah Health Information Network for their support in the effort to explore mechanisms for conducting chronic disease surveillance using electronic health records. Lastly, we would like to thank Allyn Nakashima from the Utah Department of Health and Michael King from the Centers for Disease Control and Prevention for their mentorship and contributions in the manuscript writing process.
The authors are employees of the Centers for Disease Control and Prevention, the Utah Department of Health or the Utah Health Information Network and completed this work as part of their regular job duties. No other funding was obtained for this study. All authors read and approved the final manuscript. Correspondence to Roberta Z. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
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