Machine learning models, referred to as EHRs (electronic health records) or EMRs (electronic medical records), help clinicians better manage patient health records through population management, diagnostics, and smart documentation. Clinicians also use these EHR data-based models to perform other clinical tasks.
EHR management places a heavy burden on clinicians: studies have shown that over half of a clinician’s workday can be spent on their use! The massive amount of data that EHRs require cause disruption of workflows and have limited interoperability. They also suffer from data overload when the models are not optimal. However, machine learning-based models continuously improve, helping clinicians spend less time maintaining records.
Data utilized in EHR systems includes all kind of clinical data: administrative and billing data, patient demographics, progress notes, vital signs, medical histories, diagnoses, medications, immunization dates, etc. However, artificial intelligence models go further, using the data above to analyze clinician preferences and patient feedback to improve the system.
A good machine learning model should be trained with large amounts of data from many different EHR systems. These include reports involving patient treatments, the outcomes of those treatments, and the equipment used during a patient’s visit. Additional data may include demographic information on patients.
External tools like predictive analytics and language processing also help system management and improvement.
Further external data to use in a good EHR model includes feedback forms from the Consumer Assessment of Healthcare Providers and Systems (CAHPS). Similar data from accredited health organizations are also useful.
As mentioned, EHR management imposes challenges in data input and overload as well as interoperability difficulties.
In addition to the vast amounts of EHR data, clinicians question which goals the AI-powered EHR management system should aim for. In the same vein, clinicians question which tools are most likely to help the systems reach their goals.
Flatiron Health, a data and analytics-driven cancer care service recently acquired by Roche, bought a company with a web-based EHR and tailored it to fit its OncoCloud EHR for community-based oncology.
IBM MarketScan Research Databases provides one of the oldest continually-updated collection of health claims data in the USA. Organizations use this data to prove their value to healthcare professionals, insurers, and private individuals.
The data includes drug claims, dental claims, lab results, hospital discharges, and EMR data for millions of people in the country. It also contains workplace productivity data, telling institutions how many workplaces absences they suffer and how many of their healthcare workers suffer disability due to their work.
H1 Medical Affairs Solution collects information on patients to understand their procedures and behaviors contributing to diagnosis
Definitive Healthcare’s Hospital & IDNs Database provides benchmark data for hospitals and IDNs to compare against competitors and identify growth opportunities.
Zeta-Tools Health Research conducts research among physicians, general population, and patients for marketing needs.
Graticule Medical Devices provide data sourced from EHR records to improve biomarker discovery and algorithm training for robotic surgery and other medical advances