Seventy-two percent of responding providers extract data from more than 10 platforms or interfaces--some more than 100--with EHR and billing/financial data still by far the most common. But data also comes from patient-generated sources such as portals and health risk assessments (45 percent), unstructured text (39 percent), remote monitoring devices (29 percent), health information exchanges (22 percent), mobile applications (11 percent) and genomic data (7 percent).
Still Young and Learning
Analytics remain in the early stages of maturity. Traditional common uses of analytics continue at high levels. These include quality improvement (93 percent), revenue cycle management, (91 percent), resource utilization (81 percent), and population health management (79 percent). Respondents use descriptive analytics that mine for historical or retrospective analysis at a 94 percent rate. Only 68 percent use predictive analytics to forecast outcomes, trends or performance, and this is mostly done on a monthly or quarterly basis. One-third use prescriptive analytics with sophisticated models to optimize performance and recommend specific actions. Only 20 percent of respondents analytics operations regularly integrate and coordinate at an institutional level.
Trained staff to collect/process/analyze data, along with interoperability and cost, have been common barriers to implementing an effective analytics program. Survey respondents report new challenges are emerging. These include access to external data beyond proprietary networks; cost-prohibitive work required to clean/validate/integrate external data when available, lack of funding or return on investment, increased regulations on data use and patient privacy. These trends suggest the critical need for strategic planning in implementing analytics, no matter how large or small, according to a report of survey results.