The “Little Data Problem” in Healthcare

The Big Data Problem and Visualization

The digitization of healthcare data using Electronic Healthcare Record (EHR) systems is a great boon to medical researchers. Prior to EHR systems, researchers were responsible for collecting and archiving the patient data necessary to build models for guiding healthcare decisions (e.g., the Framingham Study of Cardiovascular Health). However, with EHR systems, the job of collecting and archiving patient data is off-loaded from the researchers, freeing them to focus on the BIG DATA PROBLEM. Thus, there is a lot of excitement in the healthcare community about the coming BIG DATA REVOLUTION and computer scientists are enthusiastically embracing the challenge of providing tools for BIG DATA VISUALIZATION.

It is very likely that the availability of data and the application of advanced visualization tools will stimulate significant advances in the science of healthcare. However, will these advances translate into better patient care? Recent experiences with EHR systems suggest that the answer is "NO! Not unless we also solve the LITTLE DATA PROBLEM."

The Little Data Problem in Healthcare

Compared to the excitement about embracing the BIG DATA PROBLEM, healthcare technologists and in particular EHR developers have paid relatively little attention to visualization problems on the front end of EHR systems. The EHR interfaces to the frontline healthcare workers consist almost exclusively of text, dialog boxes, and pull-down menus. These interfaces are designed for ‘data input-output.’ They do very little to help physicians to make sense of the data relative to judging risk and making treatment decisions. For example, the current EHR interfaces do little to help physicians to ‘see’ what the data ‘mean’ relative to the risk of a cardiac event; or to ‘see’ the recommended treatment options for a specific patient.

The LITTLE DATA PROBLEM for healthcare involves creative design of interfaces to help physicians to visualize the data for a specific patient in light of the current medical research. The goal is for the interface representations to support the physician in making well-informed treatment decisions and for communicating those decisions to patients. For example, the interface representations should allow a physician to ‘see’ patient data relative to risk models (e.g., Framingham model) and relative to published standards of care (e.g., Adult Treatment Panel IV), so that the decisions made are informed by the evidence-base. In addition, the representation should facilitate discussions with patients to explain and recommend treatment options, to engender trust, and ultimately to increase the likelihood of compliance.

Thus, while EHRs are making things better for medical research, they are making the everyday work of healthcare more difficult. The benefits with respect to the ‘Big Data Problem’ are coming at the expense of increased burden on frontline healthcare workers who have to enter the data and access it through clumsy interfaces. In many cases, the technology is becoming a barrier to communications with the patients, because time spend interacting with the technology is reducing the time available for interacting directly with patients (Arndt, et al, 2017).

At Mile Two, we are bringing Cognitive Systems Engineering (CSE), UX Design, and Agile Development processes together to tackle the LITTLE DATA PROBLEM. Follow this link to see an example of a direct manipulation interface that illustrates how interfaces to EHR systems might better serve the needs of both frontline healthcare workers and patients: CVDi.

Conclusion

The major point is that advances resulting from the BIG DATA REVOLUTION will have little impact on the quality of everyday healthcare if we don't also solve the LITTLE DATA PROBLEM associated with EHR systems.