Mercy Hospital, part of the St. Louis Catholic Health System, is plugging into big data to improve the quality and efficiency of its administrative and clinical operations. Mercy continually collects data – such as lab tests, prescriptions and payments – on patients at its 24 acute-care hospitals and 700 outpatient facilities and physician offices in Arkansas, Kansas, Missouri and Oklahoma. But it didn’t have a data management infrastructure that would allow it to leverage all of that information to the fullest extent possible to improve the quality and efficiency – or overall value – of the healthcare services delivered to patients. Mercy felt it needed to upgrade to a big data environment.
Mercy was using an enterprise data warehouse from Epic, which also is the vendor of its electronic health records system called EpicCare. Epic designed its enterprise data warehouse, called Clarity, to be updated in batches from Epic’s operational database, which sits underneath the software company’s EMR. The data is a day behind what is happening in real-time, but in some cases, Mercy needed to get data sooner than a day old. Mercy also wanted to work with different types of data, such as from social media, medical devices or apps on patient smartphones.
Mercy decided to migrate to a new data infrastructure using an open source, enterprise-wide data management software framework that facilitates distributed storage and processing of many types of data in its native format across clusters of commodity hardware. The health system completed the migration to the new data management infrastructure in the Fall 2014 and began adding real-time data in July 2015. Five primary sources feed Mercy’s new data environment on between 8-9 million patients. Those sources include:
Real-time data captured as clinicians click on buttons in the Epic EHR during patient care and includes such information as lab test orders and results, vital signs and medications;
Batch data fed nightly from Epic’s Clarity data warehouse, including such information as demographic details, medical history, as well as billing and insurance data;
Batch data from Epic’s log files, which track all of the patient data that Mercy users access;
Batch data from Mercy’s enterprise resource planning system;
And a separate database with inventory information, such as medications.
Using the new data management infrastructure, Mercy has begun to improve administrative and clinical processes. One example is a project to improve medical documentation. Creating an accurate claim for a hospital stay is tied to how well physicians document every diagnosis and medical complication. While physicians typically admit patients to a hospital to treat a specific, acute medical problem – such as a heart attack or pneumonia – they may diagnose other medical problems during the patient’s hospital stay. But if a physician does not adequately document the complete picture of a patient’s hospital stay, medical documentation specialists may prepare a claim that does not reflect all of the clinical resources used to treat a patient or the true complexity of a case, leading the health system to bill for less money that it is entitled to receive.
Historically, Mercy had prepared claims after patients were discharged from the hospital, but often found it difficult to get additional information from physicians to resolve issues with documentation. Therefore, Mercy adopted an automated chart-review process in which medical documentation specialists begin work on some cases while the patient is still in the hospital and while that information is still fresh in the physician’s head.
Mercy’s data analytics team worked with documentation specialists to develop a list of more than 18 secondary diagnoses or significant complications – including sepsis, anemia, and acute kidney injury – that are often under-documented. They then developed clinical rules – such as a lab test value that might be indicative of a secondary diagnosis in order to flag patient charts for review.
Case Study Questions:
Assume that you were leading the team of data management and analytics members.
1. How would you articulate the benefits and value-realized in this new data management infrastructure to a board or steering committee during a post-implementation evaluation, both short term and long term?
2. The new data management infrastructure could very well identify possible physician documentation or behavior issues. How would you go about correcting those problems going forward so they don’t happen again, and who would be involved?
3. Identify at least 2 other areas where this (or any new enterprise-wide) data management infrastructure could possibly improve, and how.