Using Predictive Analytics to Improve Patient Care: The Role of Custom Healthcare Software
Authored By: David Taylor
As a senior software developer with over 15 years of experience developing custom healthcare solutions, I have seen firsthand how predictive analytics and customized software can transform patient care. Medical groups today have access to enormous amounts of data, from electronic health records and insurance claims to wearable devices and genomic sequencing. The key is harnessing all this data through sophisticated predictive models and software tailored to each medical group’s needs.
At Superior Digital, we work with healthcare clients to understand their strategic priorities and design software solutions to address their most pressing needs. For many, a top priority is improving patient outcomes through data-driven, coordinated care. We develop customized machine learning models and predictive algorithms that can indicate a patient’s risk of disease, identify candidates for preventive care, predict hospital readmission, recommend diagnostics or treatment plans, and much more. These predictive engines are then embedded directly into the day-to-day software tools clinicians use, including EHR, billing, and population health management systems.
For example, one client wanted to reduce avoidable hospital readmissions for patients with congestive heart failure. Our data scientists developed an ML model that uses patient demographics, vital signs, lab results and medication history from the EHR to predict each patient’s risk of readmission within 30 days of discharge. Through an API in the EHR, care managers see readmission risk scores for their patients and can prioritize follow up, schedule telehealth visits, reconcile medications or make other interventions to avoid a readmission.
Continued predictive capabilities into software development for medical groups allows for much more targeted, data-driven interventions. Patients receive care tailored to their needs, while healthcare organizations see significant cost savings, often many multiples of the cost required to develop the software. The future is incredibly promising, with continued advances in data collection through wearables and mobile apps, rapid growth in medical knowledge and predictive algorithms, and increasing customization of software based on the needs of each medical group. Overall, predictive analytics and customized healthcare software development is transforming the way we improve patient outcomes.