Monday, 05 October 2015

The Data-Driven Clinic

A few years ago, The New York Times published a story showing how Target tracked a customer’s purchases and determined she was pregnant – even before she had told her parents. Target is among a growing number of businesses that use data to study buying habits. Drugstores, for example, track how often you buy toothpaste or shampoo, then send an ad or coupon when you’re likely to run out.

ClinicTracker improves tracking and analyzing of data for data driven clinics

Marketing has become an increasingly scientific process as companies use data on your internet use, buying habits, and social media postings to try to increase the chances you’ll purchase their products. Studying consumer habits and preferences represents a form of data mining – analyzing data to find patterns and relationships. Target, Amazon, Netflix and other companies use it to sell their products. Engineers and city planners study traffic patterns as they design roads and housing developments. The rise of social media has spurred interest in analytics: When is the most effective time to post on Facebook? When will the most people see my tweet?

Analyzing digital information provides businesses the opportunity to make decisions based on hard information, not just guesswork. And that’s what appeals to many in the medical community. As doctors in the U.S. transition from paper records to EHRs, they find a goldmine of data. They and their administrators can use that digital treasure trove to provide care that is more efficient, effective, and patient-centered. Indeed, many are realizing that EHRs, with all their inherent expense and challenges, offer powerful insights into every aspect of the healthcare system, from business processes and workflow efficiencies, to diagnostic trends, provider practices, and outcomes.

ClinicTracker has long recognized the value of using data to directly affect policies and procedures. In 2009, a group of research psychologists analyzed data from 2,903 psychiatric patients enrolled in a child psychiatric clinic in Upstate New York. They wanted to know how demographic variables, diagnostic status, parental psychopathology/family history of mental disorders, and staff variables predict missed appointment rates.

The study found that while several variables discriminated between families who never missed an appointment and those that did, a self-reported history of maternal depression was by far the most powerful predictor. That one factor increased the risk for missing an appointment six-fold; explained nearly half of the variance in how many appointments a family would miss; and dwarfed all other predictors considered.

The study resulted in real change. The psychologists used data ClinicTracker exported to develop a training program for staff that reinforced the value of assessing the presence of maternal depression in every case they managed. By identifying which patients are prone to miss appointments, clinicians and clinic managers can focus on that group and monitor their compliance. Missed appointment data also allow a clinic to generate policies and procedures aimed at monitoring and reducing no-shows.

Because ClinicTracker adapts to changing mandated requirements, it constantly improves how clinicians track and analyze data. Simple, intuitive features allow practices to customize analytics and integrate data from a variety of sources. Easily accessible data make it easy to measure performance. The bottom line: Data allow clinicians to make decisions based on precise information rather than hazy impressions. And that leads to better outcomes.

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