Technology Tricks to Transform
Data into Knowledge
While there is no secret recipe that can be used to efficiently transform data into knowledge, it is not simply a matter of guessing either. Modern technology has yielded an abundant variety of technology tools and tricks to help make the transformation so much easier for today’s small medical practices. Some of the most effective opportunities include:
Electronic Health Records
Find an EHR that works with your practice instead of against it. Make charting easier using templates that encourage you to enter information into your patient database. Then query the database to find all patients meeting certain criteria so you can move on to the next step in population health management.
Use your EHR to book, reschedule, and document missed and no-show appointments. Set appointment types and duration and quickly rebook appointments to keep patients on track, and manage clinician schedules to maintain efficient staffing levels.
Access your data to view unpaid claims to make sure they are followed-up on promptly, and that issues are resolved before they can become problems. If you are the kind of person who has trouble assessing facts and figures in numerical formats, look for a system that can translate complex data into visually rich presentations that you can use to help make decisions.
Drill down on individual patient data to look for care gaps that need to be addressed, prior to the patient coming in for an appointment.
An EMR may be able to analyze data on a single patient but Population Health can provide analytics on the practice as a whole. Use a data tool that helps you to aggregate, analyze, and achieve results such as better patient care, reduced patient costs, and increased practice productivity.
Clinical Quality Measures
CQMs are the data points that CMS utilizes to measure and track the quality of health care services provided to patients. Use an EHR that helps to aggregate and submit this data in order to earn available incentives based on healthcare results.
There are three stages involved in transforming patient data into meaningful analytics: data, information and knowledge. Step one is data capture, step two is taking that data and aggregating it in some manner to withdraw information, and the final step is data analysis to uncover trends, gaps and areas in need of improvement. Medical practices which engage in these actions will be able provide a higher level of patient care, while still maintaining practice profitability.