Data summarization method for chronic disease tracking

Bearing in mind the rising prevalence of chronic medical conditions, the chronic disease management is one of the key features required by medical information systems used in primary healthcare. Our research group paid a particular attention to this specific area by offering a set of custom data collection forms and reports in order to improve medical professionals' daily routine. The main idea was to provide an overview of history for chronic diseases, which, as it seems, had not been properly supported in previous administrative workflows. After five years of active use of medical information systems in more than 25 primary healthcare institutions, we were able to identify several scenarios that were often end-user-action dependent and could result in the data related to chronic diagnoses being loosely connected. An additional benefit would be a more effective identification of potentially new patients suffering from chronic diseases.

For this particular reason, we introduced an extension of the existing data structures and a summarizing method along with a specific tool that should help in connecting all the data related to a patient and a diagnosis. The summarization method was based on the principle of connecting all of the records pertaining to a specific diagnosis for the selected patient, and it was envisaged to work in both automatic and on-demand mode. The expected results were a more effective identification of new potential patients and a completion of the existing histories of diseases associated with chronic diagnoses.

The current system usage analysis shows that a small number of doctors used functionalities specially designed for chronic diseases affecting less than 6% of the total population (around 11,500 out of more than 200,000 patients). In initial tests, the on-demand data summarization mode was applied in general practice and 89 out of 155 users identified more than 3000 new patients with a chronic disease over a three-month test period. During the tests, more than 100,000 medical documents were paired up with the existing histories of diseases. Furthermore, a significant number of physicians that accepted the standard history of disease helped with the identification of the additional 22% of the population. Applying the automatic summarization would help identify all patients with at least one record related to the diagnosis usually marked as chronic, but ultimately, this data had to be filtered and medical professionals should have the final say. Depending on the data filter definition, the total percentage of newly discovered patients with a chronic disease is between 35% and 53%, as expected.

Although the medical practitioner should have the final say about any medical record changes, new, innovative methods which can help in the data summarization are welcome. In addition to being focused on the summarization in relation to the patient, or to the diagnosis, this proposed method and tool can be effectively used when the patient-diagnosis relation is not one-to-one but many-to-many. The proposed summarization principles were tested on a single type of the medical information system, but can easily be applied to other medical software packages, too. Depending on the existing data structure of the target system, as well as identified use cases, it is possible to extend the data and customize the proposed summarization method.