June 08 2017 | 0 Comments | 132 reads Average Rating: 3
Creating a Plan of Action with Analytics
John Dewey, American educator, philosopher and social activist, once said “a problem well put is half solved.” Fortunately, data analytics can help healthcare organizations access the insight needed to come to this “well put” understanding of the various clinical challenges that members and patients face. In fact, my colleague, Dave Hom, recently explained why it is important to use a wide array of data sources to identify what chronic conditions are most prevalent in a health plan’s population in a blog entitled “Casting a Wide Data Net.”
While Dewey (and Dave) would rightly assert that understanding challenges from this perspective is a huge help, healthcare professionals still need to know where they truly can have ‘impact,’ and what actions to take to move past the ‘half solved’ mark. For example, as a nurse, while data analytics can identify high risk patients from a clinical and financial view point, I’ve always found that it is difficult to identify and prioritize which members/patients to focus on, where I can have ‘impact,’ and which care gaps to close and interventions to adopt.
Fortunately, data analytics can help on this leg of the journey as well by providing insight into not only who to focus on, but also which actions might result in the most significant impact on improving care and reducing costs. For example, when working with diabetic patients, the analysis can reveal which patients I should focus on and might also reveal that closing the gap around foot exams will have a significantly greater impact on care and costs than an eye exam or physical therapy.
Data analytics can also help healthcare organizations prioritize and develop (condition specific) programs that will ultimately result in the most significant return on their investment. Instead of developing programs to address the healthcare conditions that cost the most or are most prevalent in the population, healthcare organizations can use data analytics to identify where programs will have the greatest impact, both clinically and financially, on members or patients. For example, through data analysis a health plan might find that it has more asthmatic than diabetic members, and currently has high costs for the asthmatic members. However, the analysis could also reveal that a diabetes care management program has more potential to improve clinical care results than an asthma care management initiative. As such, the plan could focus its limited resources in the specific area that will have the most positive impact.
As a nurse who constantly seeks to help improve patient outcomes, I find analytics to be at the heart of making the right clinical decisions for my patients.
I discussed this concept in more detail during the Webinar entitled “Is Your Data and Risk Strategy Enabling You to Take on Risk Contracts?”. I invite you to check out the presentation and think about the various ways that data analytics can help to provide insight into improving the health of populations.