A comparison of a multistate inpatient EHR database to the HCUP Nationwide Inpatient Sample

Regarding the demographic data on encounters, the comparison of the NIS data set with the HF EHR population shows strong similarities. The HF EHR population is slightly younger by about a year and reported slightly fewer in-hospital deaths. The small difference in age and mortality could possibly be explained through the distribution of hospitals in the HF database. Compared to the NIS data set, small hospitals (<99 beds) are overrepresented in the HF EHR database. If larger hospitals tend to have older patients and increased mortality compared to much smaller hospitals, then this may be reflected in the results. The weighting system applied to HF for this analysis is designed to adjust for much of this variation, yet some difference may remain.

Comparisons of the NIS data set and the HF population for diagnoses and procedures show mixed results. With the exception of a few diagnoses, MDC categories in the HF EHR population were consistent with the NIS data set. Psychiatry and behavioral (Alcohol/Drug Use & Alcohol/Drug Induced Organic Mental Disorders, Mental Diseases & Disorders) diagnoses as well as obstetrics and gynecology (Diseases & Disorders Of The Female Reproductive System, Pregnancy, Childbirth & The Puerperium, Newborns & Other Neonates With Conditions Originating In Perinatal Period) diagnoses are much less frequently represented in the HF EHR population compared to the NIS data set. It is possible that these differences could be attributed to selection bias of hospitals in the HF database resulting in hospitals that see fewer psychiatry and obstetrics and gynecology cases. For example, hospitals who adopted this specific EHR may see fewer of these kinds of cases. Another, more likely, explanation is that many of those services were still using either paper-based clinical records or a separate EHR system during the study period. This would result in fewer diagnoses in these categories within the hospital EHR. Mental health, substance abuse, and reproductive/sexual health are identified as are identified as “sensitive health information” in the recommendations of the National Committee on Vital and Health Statistics to the Secretary of Health and Human Services [22]. Some participating institutions may have compartmentalized this data from the less sensitive EHR data or embraced emerging security features of the EHR that allow patients to have greater control over the privacy of their health data. Information about the frequency of procedures was generally less consistent between the data sources compared to diagnoses. Although 11 of the 15 procedure categories in the NIS data set are proportionally within 2 % of the HF population, the HF population reports many fewer procedures overall compared with the NIS data set. Prior research has identified that EHR databases have tended to capture fewer data elements related to provider orders [23], which may explain the difference. Another possible explanation is that not all contributing sites are able to supply procedure codes to the database.

There are overarching considerations to the differences between EHR databases and claims-based samples identified in our findings. First and perhaps primarily, they are different resources created and predominantly used for distinctly different purposes. Prior research has shown claims coding can be markedly different than patient problems captured by providers [1, 24, 25]. Neither claims nor medical records are primarily collected for research so one is not necessarily more ‘correct’ than the other. Claims data in general has the advantage of years of validation and research use. Comparably, questions of internal validity and data integrity surround EHR data. Researchers are beginning to address many of these issues by developing innovative data infrastructure and study designs [14, 26]. In contrast, the HCUP NIS is a validated and trusted data source; however, it lacks much of the detailed clinical information that is available through large, longitudinal, detailed EHR databases.

Differences between claims-based data and EHR data found in our analyses may have implications for quality of care reporting since many federal measures are derived from claims data. Many federal-based quality reporting measures are based on claims data and the significance of the differences in reporting quality measures through the EHR are not well understood. For example, EHRs typically have more diagnosis fields (we found nearly 100 in this sample) compared to claims data, which may be significant when excluding cases from quality measures. Also, concepts are documented in claims data that are not documented in EHRs, and vice versa.

Additionally, EHR data may capture health care provided to patients yet not billed for that may not be reflected in the claims-based data, which could affect the comparison between the two data sets. The EHR sample included uninsured and self-pay patients, which the claims data do not have. Although EHRs may be more comprehensive in this regard, it complicates the comparison between the two. Finally, this analysis focused on in-patient hospitalizations; comparison of outpatient data might yield different results.

EHR data is a new type of data with new research possibilities, rather than being used as a supplement or substitute for claims based study designs. For example, EHR data may be preferred to claims data when clinical outcomes such as laboratory tests are required, or a level of clinical specificity is needed. EHR data may be preferred when the complete lists of diagnosis are needed, as many claims data will use only the first 25 or so diagnosis. Some data from the EHR such as problem lists, reflect clinician assigned diagnoses as they work the case, rather than codes assigned by a professional medical coder.