Abstract
BackgroundLarge healthcare databases, with their ability to collect many variables from daily medical practice, greatly enable health services research. These longitudinal databases provide large cohorts and longitudinal time frames, allowing for highly pragmatic assessment of healthcare delivery. The purpose of this paper is to discuss the methodology related to the use of the United States Military Health System Data Repository (MDR) for longitudinal assessment of musculoskeletal clinical outcomes, as well as address challenges of using this data for outcomes research.MethodsThe Military Health System manages care for approximately 10 million beneficiaries worldwide. Multiple data sources pour into the MDR from multiple levels of care (inpatient, outpatient, military or civilian facility, combat theater, etc.) at the individual patient level. To provide meaningful and descriptive coding for longitudinal analysis, specific coding for timing and type of care, procedures, medications, and provider type must be performed. Assumptions often made in clinical trials do not apply to these cohorts, requiring additional steps in data preparation to reduce risk of bias. The MDR has a robust system in place to validate the quality and accuracy of its data, reducing risk of analytic error. Details for making this data suitable for analysis of longitudinal orthopaedic outcomes are provided.ResultsAlthough some limitations exist, proper preparation and understanding of the data can limit bias, and allow for robust and meaningful analyses. There is the potential for strong precision, as well as the ability to collect a wide range of variables in very large groups of patients otherwise not captured in traditional clinical trials. This approach contributes to the improved understanding of the accessibility, quality, and cost of care for those with orthopaedic conditions.ConclusionThe MDR provides a robust pool of longitudinal healthcare data at the person-level. The benefits of using the MDR database appear to outweigh the limitations.
Highlights
Large healthcare databases, with their ability to collect many variables from daily medical practice, greatly enable health services research
We recently identified a cohort of patients that received arthroscopic hip surgery within the United States Military Health System (MHS) over a 10 year period
The following are the data files from the Military Health System Data Repository (MDR) we found most relevant to the study of musculoskeletal conditions, and stored in the MDR in Statistical Analysis System (SAS) format: 1. Standard Inpatient Data Record (SIDR): This information includes diagnosis and procedure codes, length of stay, cost and relative weighted products for each episode of care, and departments rendering care for every inpatient hospital admission that takes place within a military treatment facilities (MTF)
Summary
With their ability to collect many variables from daily medical practice, greatly enable health services research. These longitudinal databases provide large cohorts and longitudinal time frames, allowing for highly pragmatic assessment of healthcare delivery. Big data in healthcare is defined as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of informationprocessing that enable enhanced insight, decision making and process automation [2, 3].”. Data is captured from many sources at a real time rapid pace known as velocity This velocity, along with variety of data, creates a significant challenge for cleansing and analyzing the data. These datasets can be so large, overwhelming, and complex that traditional software and hardware are insufficient [2]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
