12 Oct Big Data Using Predictive Analytics Aims To Improve Medication Adherence

Neil Smiley

MedicalResearch.com Interview with:
Neil Smiley
Founder and CEO of Loopback Analytics

Editor’s note: Loopback Analytics mission is to “integrate data across a myriad of healthcare information systems to bridge the expanding gaps within the care continuum”. CEO Neil Smiley discusses the problem of medication adherence and possible means to address the issue.

MedicalResearch.com: What is meant by medication “adherence”? How big a problem does this represent in term of health care outcomes and costs?

Response: Medication adherence is the degree to which a patient is taking medications as prescribed. Poor medication adherence takes the lives of 125,000 Americans annually, and costs the health care system nearly $300 billion a year in additional doctor visits, emergency department visits and hospitalizations.

MedicalResearch.com: What can be done by health care providers, systems and pharmacists to improve medication adherence?

Response: There are many potential failure points after a prescription is written, that range from affordability, transportation, literacy, confusion over brand vs. generics, duplication of therapy. Many patients simply stop taking medications when they start feeling better or fail to refill chronic maintenance medications. Healthcare providers can improve adherence by anticipating and eliminating potential points of failure before they become problems. For example, high risk patients leaving the hospital are less likely to be readmitted if they get their prescriptions before they are discharged. Follow-up consultations by pharmacists can assist patients with side effects that may otherwise cause patients to abandon their treatment plan and provide patients with education on how to take medications correctly.

MedicalResearch.com: How will access to big data facilitate the ability to improve adherence?

Response: Big data analytics, which incorporate medication refill patterns, encounter data and symptoms can be used to proactively identify patients that are at highest risk for medication adherence failure. For example, gaps in refills of chronic maintenance medications strongly correlates with risk of re-hospitalization. Big data analytics enable providers to manage adherence initiatives at a population level and direct scarce resources to engage patients before problems escalate.

MedicalResearch.com: What steps is Loopback Analytics taking in this regard?

Response: At Loopback Analytics, we leverage predictive analytics to proactively identify patients most likely to be re-hospitalization due prior medication adherence difficulties or complex medication regimens, and match them to a variety of interventions such as bedside delivery of medications prior to hospital discharge, analysis of medications for adverse events and personalized follow-up from a pharmacist. We then monitor and analyze each intervention action from identification to engagement to measure effectiveness in improving patient outcomes.

MedicalResearch.com: Is there anything else you would like to add?

Response: Medication Adherence continues to be one of the largest challenges facing healthcare today. With changes in reimbursement models, better data analytics, and closer collaboration with care providers, we have a terrific opportunity to improve medication adherence rates, leading to lower costs across healthcare settings and to healthier patient lives.

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Note: Content is Not intended as medical advice. Please consult your health care provider regarding your specific medical condition and questions.

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Last Updated on October 12, 2016 by Marie Benz MD FAAD