Health Data

Combatting the Opioid Crisis with Data

By Wesley Combs and David Morin, MD, FACP, CPI, FACRP

Even as states across the economically challenged Appalachian region of the US gain a foothold in combatting the opioid crisis, rates of opioid deaths in Tennessee are rising, reaching the state’s deadliest year on record in 2018.1

Tennessee is among the hardest-hit states facing the nation’s opioid epidemic, with more than six million opioid prescriptions issued statewide and 1,818 opioid deaths in 2018, the highest since 2014. The rate of death involving opioids reached 19.3 per 100,000 residents in 2017.

While opioid deaths decreased by four percent nationally and decreased even more in Appalachian states like Ohio (22.1 percent), Pennsylvania (18.8 percent), West Virginia (11.4 percent), and Kentucky (14.7 percent), Tennessee struggles to drive better health outcomes for those suffering with opioid-use disorder.2

What’s holding Tennessee back from helping residents overcome this epidemic? One answer is lack of ready access to actionable information at the point of care related to warning signs of opioid-use disorder.

In neighboring states like Virginia, access to a controlled substance monitoring database is easily available through the electronic health record (EHR). With a single sign-on, a prescriber or pharmacist can view a patient’s prescription history directly within their workflows.3 Through Virginia’s system—which is connected to a prescription drug monitoring program (PDMP) connecting 22 states, including Tennessee—clinicians and pharmacists are empowered to make safer prescribing and dispensing decisions within existing workflows.

In Tennessee, however, the controlled substance monitoring database is not linked to the EHR with a single sign-on. That means physicians must obtain this data through a separate system.

In addition, even as community health information exchanges (HIEs) in some areas of Tennessee give providers insight into patients’ comprehensive medical history—including recent emergency department (ED) visits or consultations with specialists—not all providers in Tennessee are linked to a community HIE. Those that don’t possess a community HIE may miss signs that could point to a potential substance use disorder, such as:

  • An extraordinarily high number of ED visits
  • Visits to numerous physicians in one year
  • Frequent inpatient hospital stays
  • An opioid prescription—currently or in the past year
Without unencumbered access to both a patient’s comprehensive medical record and the state’s controlled substance monitoring database at the point of care, physicians are hard-pressed to make critical connections that point to opioid-use disorder. This limits their ability to provide the right intervention for the right patient at the right time.
Case Study: One Provider’s Experience
How could widespread adoption of a community HIE—combined with state data monitoring prescriptions for opioids—strengthen Tennessee’s front-line defense against opioid-use disorder? One provider, Holston Medical Group (HMG), a regional medical group serving more than 200,000 patients in northeast Tennessee, southwest Virginia, and North Carolina found three distinct advantages.

Enhanced Collaboration Among Providers

In 2012, HMG invested in the only bidirectional common medical record system in the state of Tennessee, inviting providers throughout eastern Tennessee, southwest Virginia, and the Charlotte, NC, region to participate. Today, more than 1,200 physicians are connected through the community HIE, which:

  • Pushes automatic notifications to physicians regarding patients’ recent care interactions
  • Helps providers more easily identify patients who show signs of complex health issues
  • More tightly manages care for their sickest patients—the five to 10 percent of patients who typically account for 50 percent of healthcare costs
One example of the way in which a community HIE supports better care for patients with complex needs is the ability to better coordinate care for oncology patients. For example, patients who have cancer see specialists outside of HMG’s multispecialty practice. The ability to review these specialists’ notes when these patients arrive with ailments seemingly unrelated to their disease empowers physicians to look for connections between their oncology treatment and the new health issues they face.

Among those suffering with opioid-use disorder, 18.7 percent are estimated to be taking prescription opioids, and they receive 51.4 percent of the nation’s opioid prescriptions, according to a Journal of the American Board of Family Medicine study.4 Adequate treatment for one in five patients will require not only specialized addiction resources—such as medication-assisted treatment, counseling, and behavioral therapies—but also treatment of any underlying mental health conditions. This requires tightly integrated care, which is most effectively supported by shared data across providers and care settings.

Data-Fueled Innovations for Opioid Treatment
Company Product Name Category
Algomet Rx, Inc. Rapid Drug Screen Monitoring
Avanos Withheld per company request Other
Brainsway, Ltd Brainsway Deep Transcranial Magnetic Stimulation Device (DTMS) Opioid Use Disorder Therapy
CognifiSense, Inc. Virtual Reality Neuropsychological Therapy (VRNT) Pain Therapy
iPill Dispenser iPill Dispenser Medication Dispensing
Masimo Corporation Withheld per company request Overdose Therapy
Milliman Opioid Prediction Services Diagnostic
ThermoTek, Inc. NanoThermTM and VascuThermTM Systems Pain Therapy
Source: Food and Drug Administration. “FDA Innovation Challenge: Devices to Prevent and Treat Opioid Use Disorder.” July 9, 2019. https://www.fda.gov/about-fda/cdrh-innovation/fda-innovation-challenge-devices-prevent-and-treat-opioid-use-disorder.
Mining Data for Predictive Analysis

Pairing HIE data with predictive analytic capabilities provides more complete data for risk stratification, real-time assessment of the most impactful areas in which to focus care interventions, and more effective care management. It empowers providers to determine which patients need to be seen immediately, enabling a sophisticated form of care triage and more efficient use of limited care resources. It can even predict which patients are likely to end up in the ED or are headed for an inpatient stay, as well as the types of interventions that could help keep them out of the hospital. In Tennessee, the impact in fighting the opioid epidemic could be substantial, giving providers:

  • The ability to classify patients according to their risk of developing opioid-use disorder or other forms of substance dependence5
  • Increased understanding of the link between opioid use and suicide risk6
  • Algorithms that predict which patients are most likely to experience opioid overdose7
At HMG, adoption of a community HIE enabled physicians to stratify patient populations into risk levels based on their number of chronic conditions and recent ED visits or hospital admissions. This allows for the most appropriate allocation of care resources and deployment of outreach efforts that help ensure adherence to care plans. Physicians also gain the ability to pinpoint trends in critical data (e.g., A1C levels, blood pressure) from various sources and eliminate duplicative care and service.

The impact has been significant: HMG experienced improved performance around risk identification under a commercial health plan contract; a nearly 10 percent improvement in hospital admissions, with admissions-per-1,000 patients that are 20 percent below market rates; ED utilization that is 25 percent lower than the market average; and a 4.2 percent increase in evaluation and management services, which reflects use of the right care in the right setting.

Data for Development of Specialized Programs Combatting Opioid Addiction

Across the country, healthcare leaders are seeking ways to use data to develop innovative ways to tackle the opioid epidemic. For example, in June 2019, the US Food and Drug Administration issued an innovation challenge to spur development of digital health technologies for preventing and treating opioid-use disorder. More than 250 applications were received, and eight medical devices designed to detect opioid overdose, dispense medication, and provide pain treatment alternatives to opioids will receive FDA support in accelerating the timeline for development (see the side bar above).

At HMG, data from the community HIE supported development of programs to address the needs of complex patient populations. Such programs include an extensivist clinic, staffed by hospitalists and nurses, that helps patients suffering from renal disease, heart disease, lung disease, and other issues receive the care they need while avoiding an inpatient hospital stay. The extensivist clinic is a big contributor to HMG’s success under value-based payment models, which are tied to 30 percent of reimbursement for the practice. Pay-for-value payments have increased 44 percent since implementing the clinic in 2013.

Changing Opioid Outcomes with Data
As providers across Tennessee seek ways to strengthen the state’s response to the opioid epidemic, data exchanged between healthcare providers will be vital to determining the right interventions for the right patients at the right time. Widespread adoption of a community health information exchange could be a critical tool in reversing the rates of opioid disorder and opioid overdose deaths and driving better health outcomes.
Notes
  1. Kelman, Brett. “Tennessee has deadliest year yet for drug overdoses, as nearby states improve.” Nashville Tennessean, October 18, 2019. www.tennessean.com/story/news/health/2019/07/19/opioid-crisis-tennessee-overdose-deaths-climbing-heroin-fentanyl-meth/1550137001.
  2. Ibid.
  3. Virginia Department of Health Professions. “Prescription Monitoring Program PMP Toolkit.” www.dhp.virginia.gov/PractitionerResources/PrescriptionMonitoringProgram/PublicResources/EducationToolkit.
  4. Davis, Matthew A. et al. “Prescription Opioid Use among Adults with Mental Health Disorders in the United States.” Journal of the American Board of Family Medicine. July 2017. www.jabfm.org/content/30/4/407.
  5. Ellis, Randall J. et al. “Predicting opioid dependence from electronic health records with machine learning.” Bio Data Mining. January 29, 2019. https://biodatamining.biomedcentral.com/articles/10.1186/s13040-019-0193-0.
  6. Kaiser Permanente. “Study to examine the role of opioid use in suicide risk.” August 16, 2018. https://about.kaiserpermanente.org/our-story/health-research/news/new-kaiser-permanente-study-will-examine-the-role-of-opioid-use-.
  7. Lo-Ciganic, Wei-Hsuan et al. “Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.” JAMA Open Network. March 22, 2019. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2728625.
 

Wesley Combs (wesley.combs@myhmg.com) is chief information officer, Holston Medical Group, and CEO, OnePartner. David Morin (david.morin@myhmg.com) is director of clinical research and practicing physician, Holston Medical Group.

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Tags: health data , health information exchange , opioid crisis , opioids