Automating Healthcare
Solving business problems with savvy automation

Chronic Disease Registries

Business problem
Managing the care of patients with chronic diseases is challenging for busy primary care physicians. Every member of the care team needs a systematic way to track issues, treatments and outcomes. Managing the disease in a population presents a different challenge. An electronic medical record (EMR) is good at tracking the care of individuals, but not necessarily for a population. With or without an EMR, special tools are needed.

Managing without an EMR
One of the first requests of our team was to develop tools to help manage the care of patients with three chronic diseases:

  • Pediatric asthma
  • Diabetes mellitus
  • Depression

We were still in the early stages of implementing the Epic ambulatory EMR, and it would be at least two or three years before most physicians had full EMR access. Waiting for the full EMR was clinically not acceptable. Reluctantly, we concluded that we had to build a sort of mini-EMR, knowing that it would be discarded once the full EMR was implemented.

The clinical team working on pediatric asthma was the furthest along with planning, so the asthma registry was the first to be built. One of the first issues was determining which data elements could be extracted and displayed from the Meditech system, and which would need to be manually input into the registry:

  • Demographics and encounter history were available from Meditech
  • Lab test results, medications and allergies were not extractable from Meditech

This meant that we needed a way to manually input the data that could not be extracted from Meditech. The first step was to have each pediatrician identify and register all patients with asthma in his/her panel (click the partial form below to see the full registration form).

[Click the image above to see the full size image]

Once registered, detailed data for each patient were input through a set of interactive forms, as listed along the right of the screen shown below (click the image below to see the full size screen). Each form (as selected from buttons on the right) displayed data relevant to the specific encounter (as selected on the left).

[Click the image above to see the full size image]

The action plan is an absolutely essential tool to help asthmatic patients and their families manage their health. Producing an on-demand, printable action plan (click the image below to see a full plan) that exactly matches the state-approved form was a challenge.

[Click the image above to see the full size image]

A set of management tools were built to help providers manage their pediatric asthma patients:

Diabetes mellitus
The second registry was to track the care of adult diabetes patients. The user interface used the same framework as the asthma registry, with some different forms listed on the right side (click the image below to see the full-size screen).

[Click the image above to see the full size image]

The diabetes work sheet is arguably one of the two most important tools in this registry, providing a way to track A1c scores in the context of many other measures (click the image below to see the full-size screen).

[Click the image above to see the full size image]

The other essential tool was the diabetes goal sheet. Like the asthma action plan, the diabetes goal sheet is primarily a tool for the patient (shown below).

A set of management tools were built to help providers manage their diabetes patients:

General tools
In addition to the disease-specific registries, two tools were created to generate lists of patients with:

  • scheduled appointments or
  • missed appointments,

using any combination of the variables shown below.

Depression registry sputters
Despite two efforts, the depression registry never really achieved significant traction prior to the Epic implementation. Among the reasons:

  • Least well-developed workflow of the three diseases, complicated by the fact that two clinical departments were involved
  • Difficulty agreeing on a security model (i.e., which users of the asthma and diabetes registries should know that a patient is in the depression registry and/or be able to see the depression registry contents)
  • Registry fatigue — busy primary care providers were already stretched trying to fully utilize the first two registries

Managing with an EMR
Once the Epic ambulatory suite was implemented, registry tools related to individual patients were redundant. However, the registry tools for managing patient panels or larger populations were still very important because the Epic EMR is designed to manage the care of individuals, not groups. By far the most frequently used item now is the diabetes worksheet (shown above). Several of the diabetes reports listed above are also used quite frequently (listed below in descending order of popularity):

  • Diabetes HgbA1c Distribution Aging Report
  • Diabetes Eye Visits Aging Report
  • Provider Report
  • Diabetes HgbA1c Distribution Report
  • Diabetes LDL Distribution reports
  • Diabetes Microalbumin Distribution reports

The other frequently-used tools are the dashboards:

Future registries
As can be deduced from the ADHD dashboard, a fourth chronic disease registry has been added and more are planned. Like the ADHD registry, new registries will follow a simpler model. Patients will be identified by an automated process examining diagnoses, medications and/or test results, and automatically added to the relevant registry. A registry dashboard (and perhaps another report or two) will be created to help manage the care of registered patients in groups such as primary care panels.

The three original registries had quite different results. The asthma registry was the most successful.

  • For patients who were enrolled in the asthma registry for two years, there was a statistically significant drop of 50% in asthma-related emergency department visits when comparing the one year period prior to enrollment in the registry with the second year of enrollment in the registry.
  • Asthma-registry patients also showed a statistically significant drop of 45% in asthma-related hospitalizations for the same time periods.
  • Diabetes registry patients showed mixed results, with some improvements but nothing significant enough to be compelling.

Lessons learned

  • One of the early, critical design decisions for the registries was how to handle security for different registries. Because there was no overlap in patients between the asthma and diabetes registries, and the depression model was undefined, we decided not to try to partition each registry from the others. That was a mistake. When we finally were ready to work on the depression registry, re-engineering the security model for all the registries was a huge effort. It would have been better, in the absence of a clear consensus about how to handle security, to take a conservative approach and build clear partitions between all the registries.
  • Regardless of benefits offered, we found it extremely difficult to get providers to input data in the pre-EMR registries. Everyone was busy and reluctant to accept another administrative task. The quality management department tried to fill the gaps with temps to input data on behalf of providers, but this was an unsatisfactory workaround, and a real solution was never found.
  • Training providers in registry use must be identified from the beginning as a significant commitment, and resources allocated. As so often happens with training, we tried to handle it without adding expense, and quickly learned that it was a much larger task than anyone anticipated.

Posted 13 May 2008


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