Automatic Medical Records Reconciliation from ATAAPS, DMHRSi and MHS Genesis with Machine Learning Rules Processing

The Air Force must maintain a technical edge specifically when it comes to decision-making regarding budget and resource allocation. Further, the 60th Medical Group (MDG) has a national defense-related mission need in the area of automatic curation, reconciliation & integration of Military Treatment Facility (MTF) data for analysis and reporting of MTF performance metrics. Specifically, the relative efficiency of MTFs in producing patient care is a critical factor in evaluating MTF performance in comparison within each military service, among the military services, and against purchased private-sector care. It is imperative that performance measurements are correctly captured and properly account for the amount of patient care provided and the costs involved. Three new requirements make this need timely:  1) Military Health System reform and realignment,  2) MHS Genesis electronic medical record installation, and  3) Military Health System medical end force changes.  Each of these new requirements alone makes proper measurement and reporting imperative. Because all three are occurring at the same time, it is extremely important we get data curation, reconciliation and integration, and related analysis and reporting done right.  The chain of command (i.e. Office of the Assistant Secretary of Defense (OASD(HA)), Defense Health Agency (DHA), and Air Force Surgeon General (AF/SG)) monitors the amount of patient care provided by MTFs and uses the information to determine the MTF medical budget. This ultimately impacts resource allocation and potential MTF realignment.  Defense Medical Human Resources System internet (DMHRSi) is a key component of data, analysis, and reporting. DMHRSi measures hours worked by individuals (e.g. clinicians) and work areas (e.g. medical specialty areas). In this context, patient care is an important metric (direct, indirect, and other patient care). Other personnel (e.g. clinician) work that is tracked includes meetings, graduate medical education, training, absences, and other activities.  Under-reporting of actual hours would tend to increase the apparent efficiency of the MTF because over-time hours would not be charged to the workload. However, it would also misrepresent the actual resource needs of the MTF. Misreporting among work centers within an MTF would affect comparisons within an MTF or among similar work centers at different MTFs. Again, this makes it difficult to get an accurate picture of resource and budget needs. A larger problem is misreporting hours worked on patient care instead of readiness activities, for example, which could make some MTFs look more inefficient than others. Extra hours billed to patient clinical care instead of readiness, or administrative duties makes an MTF appear to be less efficient because the costs of FTE labor input appear greater than the recorded patient workload output.