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5 Ways Allscripts Will Help Fight Opioid Abuse In 2018

Posted on May 22, 2018 I Written By

The following is a guest blog post by Paul Black, CEO of Allscripts, a proud sponsor of Health IT Expo.

Prescription opioid misuse and overdoses are on the rise. The Centers for Disease Control and Prevention (CDC) reports that more than 40 Americans die every day from prescription opioid overdose. It also estimates that the economic impact in the United States is $78.5 billion a year, including the costs of healthcare, lost productivity, addiction treatment and criminal justice involvement.

The opioid crisis has taken a devastating toll on our communities, families and loved ones. It is a complex problem that will require a lot of hard work from stakeholders across the healthcare continuum.

We all have a part to play. At Allscripts, we feel it is our responsibility to continuously improve our solutions to help providers address public health concerns. Our mission is to design technology that enables smarter care, delivered with greater precision, for better outcomes.

Here are five ways Allscripts plans to help clinicians combat the opioid crisis in 2018:

1) Establish a baseline. Does your patient population have a problem with opioids?

Before healthcare organizations can start addressing opioid abuse, they need to understand how the crisis is affecting their patient population. We are all familiar with the national statistics, but how does the crisis manifest in each community? What are the specific prescribing practices or overdose patterns that need the most attention?

Now that healthcare is on a fully digital platform, we can gain insights from the data. Organizations can more precisely manage the needs of each patient population. We are working with clients to uncover some of these patterns. For example, one client is using Sunrise™ Clinical Performance Manager (CPM) reports to more closely examine opioid prescribing patterns in emergency rooms.

2) Secure the prescribing process. Is your prescribing process safe and secure?

Electronic prescribing of controlled substances (EPCS) can help reduce fraud. Unfortunately, even though the technology is widely available, it is not widely adopted. Areas where clinicians regularly use EPCS have seen significantly less prescription fraud and abuse.

EPCS functionality is already in place across our EHRs. While more than 90% of all pharmacies are EPCS-enabled, only 14% of controlled substances are prescribed electronically. We’re making EPCS adoption one of our top priorities at Allscripts, and we continue to discuss the benefits with policymakers.

3) Provide clinical decision support. Are you current with evidence-based best practices?

We are actively pursuing partnerships with health plans, pharmaceutical companies and third-party content providers to collaborate on evidence-based prescribing guidelines. These guidelines may suggest quantity limits, recommendations for fast-acting versus extended-release medications, protocols for additional and alternative therapies, and expanded educational material and content.

We’ll use the clinical decision support technologies we already have in place to present these assessment tools and guidelines at the time needed within clinical workflows. Our goal is to provide the information to providers at the right time, so that they can engage in productive conversations with patients, make informed decisions and create optimal treatment plans.

4) Simplify access to Prescription Drug Monitoring Programs (PDMPs). Are you avoiding prescribing because it’s too hard to check PDMPs?

PDMPs are state-level databases that collect, monitor and analyze e-prescribing data from pharmacies and prescribers. The CDC Guidelines recommend clinicians should review the patient’s history of controlled substance prescriptions by checking PDMPs.

PDMPs, however, are not a unified source of information, which can make it challenging for providers to check them at the point of care. The College of Healthcare Information Management Executives (CHIME) has called for better EHR-PDMP integration, combined with data-driven reports to identify physician prescribing patterns.

In 2018, we’re working on integrating the PDMP into the clinician’s workflow for every patient. The EHR will take PDMP data and provide real-time alert scores that can make it easier to discern problems at the point of care.

5) Predict risk. Can big data help you predict risk for addiction?

Allscripts has a team of data scientists dedicated to transforming data into information and actionable insights. These analysts combine vast amounts of information from within the EHR, our Clinical Data Warehouse – data that represents millions of patients – and public health mechanisms (such as PDMPs).

We use this “data lake” to develop algorithms to identify at-risk patients and reveal prescription patterns that most often lead to abuse, overdose and death. Our research on this is nascent, and early insights are compelling.

The opioid epidemic cannot be solved overnight, nor is it something any of us can address alone. But we are enthusiastic about the teamwork and efforts of our entire industry to address this complex, multi-faceted epidemic.

Hear Paul Black discuss the future of health IT beyond the EHR at this year’s HIT Expo.

Study Offers EHR-Based Approach To Predicting Post-Hospital Opioid Use

Posted on March 27, 2018 I Written By

Sunny is a serial entrepreneur on a mission to improve quality of care through data science. Sunny’s last venture docBeat, a healthcare care coordination platform, was successfully acquired by Vocera communications. Sunny has an impressive track record of Strategy, Business Development, Innovation and Execution in the Healthcare, Casino Entertainment, Retail and Gaming verticals. Sunny is the Co-Chair for the Las Vegas Chapter of Akshaya Patra foundation (www.foodforeducation.org) since 2010.

With opioid abuse a raging epidemic in the United States, hospitals are looking for effective ways to track and manage opioid treatment effectively. In an effort to move in this direction, a group of researchers has developed a model which predicts the likelihood of future chronic opioid use based on hospital EHR data.

The study, which appears in the Journal of General Internal Medicine, notes that while opioids are frequently prescribed in hospitals, there has been little research on predicting which patients will progress to chronic opioid therapy (COT) after they are discharged. (The researchers defined COT as when patients were given a 90-day supply of opioids with less than a 30-day gap in supply over a 180-day period or receipt of greater than 10 opioid prescriptions during the past year.)

To address this problem, researchers set out to create a statistical model which could predict which hospitalized patients would end up on COT who had not been on COT previously. Their approach involved doing a retrospective analysis of EHR data from 2008 to 2014 drawn from records of patients hospitalized in an urban safety-net hospital.

The researchers analyzed a wide array of variables in their analysis, including medical and mental health diagnoses, substance and tobacco use, chronic or acute pain, surgery during hospitalization, having received opioid or non-opioid analgesics or benzodiazepines during the past year, leaving the hospital with opioid prescriptions and milligrams of morphine equivalents prescribed during their hospital stay.

After conducting the analysis, researchers found that they could predict COT in 79% of patients, as well as predicting when patients weren’t on COT 78% of the time.

Being able to predict which patients will end up on COT after discharge could prove to be a very effective tool. As the authors note, using EHR data to create such a predictive model could offer many benefits, particularly the ability to identify patients at high risk for future chronic opioid use.

As the study notes, if clinicians have this information, they can offer early patient education on pain management strategies and where possible, wean them off of opioids before discharging them. They’ll also be more likely to consider incorporating alternative pain therapies into their discharge planning.

While this data is exciting and provides great opportunities, we need to be careful how we use this information. Done incorrectly it could cause the 21% who are misidentified as at risk for COT to end up needing COT. It’s always important to remember that identifying those at risk is only the first challenge. The second challenge is what do you do with that data to help those at risk while not damaging those who are misidentified as at risk.

One issue the study doesn’t address is whether data on social determinants of health could improve their predictions. Incorporating both SDOH and patient-generated data might lend further insight into their post-discharge living conditions and solidify discharge planning. However, it’s evident that this model offers a useful approach on its own.