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Big Data in Healthcare: Providing Better Patient Care

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Someone talking about big data in healthcare

Better data can help you achieve better outcomes. Here’s a few suggestions.

Small steps can have a big impact.

Agencies that leverage big data will help their patients heal more efficiently and effectively. With more information than ever at your disposal, it can be overwhelming to dive in, not to mention the list of barriers to implementing data analytics range from different types of analytics data silos to outdated IT systems.

Agencies need to start planning for how they’ll embrace big data now because the future will only grow more dependent on these technologies. Fortunately, taking small steps now can make a big impact and result in more efficient operations and better patient outcomes.

Turning data into actionable, health insights.

The hurdle for most mental health and substance use agencies and for that matter, most businesses, is not understanding the importance of data analytics but how to make the data actionable. Most already have the most important tool: an EHR that helps track behavioral health and agency data.

Yet, there are many hurdles that agencies have to jump before making big data in healthcare and analytics actionable, including staffing and skill gaps, health data exchange barriers, and general organizational planning challenges. But before your agency dives into breaking down data silos and rearranging how you operate, it’s helpful to understand the three main types of analytics used in healthcare.

Descriptive Analytics

Documents Historical Data

This is a great place for agencies to start if they’re new to using analytics. Like its name implies, descriptive analytics describe what happened in the past by referencing historical data records. You’re likely already utilizing descriptive analytics without realizing it if you’ve looked for when your busiest time of the day is for walk ins and been able to answer it using your own data records.

Descriptive analytics are especially useful to translate cold, hard numbers into something understandable. However, it does not help the user predict future occurrences or future trends.

Predictive Analytics

Describes Future Risk Detection

Predictive analytics are what most people think of when they hear the word “analytics.” This type uses descriptive data to predict what could happen in the future. For this predictive analytics to be accurate and useful, large amounts of descriptive data must be fed into it,as close to real time as possible. From there, predictive analytics use algorithms to spit out trend lines or risk scores that agencies can use to see what might be coming around the corner. Two main challenges to implementing predictive analytics are data silos as it requires huge amounts of cross-departmental and cross-agency data as well as strong IT capabilities. Predictive analytics can be used not just to help treat patients more accurately based on historical data but can also be applied to an agency’s financial, administrative, and data security systems to keep organizations safer, more efficient, and more effective.

In the mental health world, predictive analytics are being applied to help prevent suicide. According to Kaiser Permanente, utilizing the data stored in EHRs provides a powerful “treasure trove to support risk detection.” They conducted a study with Mental Health Research Network, utilizing EHR data. They discovered that: “In the 90 days following an office visit, suicide attempts and deaths among patients in the highest 1 percent of predicted risk were 200 times more common than among those in the bottom half of predicted risk.”

To find this, the research team incorporated data points like prior suicide attempts, diagnoses, prescriptions, inpatient or emergency room care, and scores from a standardized depression questionnaire. Other models that do not rely on predictive analytics and use fewer data points are less accurate. First author on the report, Gregory E. Simon, MD, MPH said that “We demonstrated that we can use electronic health record data in combination with other tools to identify people at high risk for suicide attempt or suicide death.”

While it may not be surprising that those who were considered “high risk” were indeed at a higher risk of self-harm, it proves how powerful predictive analytics can be. If mental health and substance use health providers are able to tell when a patient is more likely to hurt themselves, they can adjust their sessions and medications accordingly.

Prescriptive Analytics

Points to Path of Maximum Benefit

Finally, there are prescriptive analytics, which are the next step on an agency’s analytical journey. Beyond predicting a probable future, prescriptive analytics can also show what the most likely path to maximum benefit is. This step in the journey is beyond the reach of most healthcare agencies, be they mental health, substance use, or medical. A main reason for this is simply that prescriptive analytics are new and require so much data that the systems just aren’t in place, yet.

However, once the systems are in place to make prescriptive analytics more ubiquitous, the possibilities are staggering. For instance, therapists can treat a patient based on both their clinical situation and genetic makeup and know, before they begin, what treatment is most likely to help. People will be helped faster and holistically, agencies will operate more efficiently, and the general population will be able to receive individualized treatment plans thus reducing strain on mental health, substance use, and medical facilities.

5 Steps Your Agency Can Take to Start Implementing Analytics

Regardless of what stage your agency is at, to move to the next one you need a plan. Start with something easy such as these simple steps.

  • Form an internal team tasked with implementing analytics to make sure it doesn’t get pushed to the side.
  • Take inventory of what data and analytics you currently have.
  • Determine if you can connect data sets to create information and if you have the technology available to do so.
  • Begin to apply statistical methodology to your data to answer key questions you have.
  • Use that data to tell a story so it is clearly understandable to every stakeholder.

Pro Tip:
Your data needs to be easily attainable, easily understandable, and the report easily repeatable to make it useful. Invest upfront in the resources that will help you do this, like EHRs and statistical tools, and soon you’ll be using analytics to inform every aspect of your business operations and patient care.

The Bottom Line

Having access to big data and implementing analytical processes is no longer a competitive edge, it’s a business imperative. Agencies that are able to fine tune their clinical and operational practices will be financially healthy and be able to more effectively treat their patients. Similarly, patients will begin to expect their health providers to use their data in a holistic manner as they become more educated about the power of data.

Agencies that don’t implement these practices will soon fall behind, which is a shame as it will hurt providers and patients alike. Embracing data analytics can be overwhelming but it doesn’t have to be. Taking small steps and finding the right EHR solution for your agency will set you up for long-term success and prepare your business for the data-fueled future.

Procentive is an industry-leading EHR for behavioral health agencies. We’re committed to providing top-notch service so you can have access to your data, exactly when you need it and get more time back in your day. Contact us today to learn more.

Spend More Time Caring with Procentive's Ful-Featured EHR for Substance Use Recovery Agencies


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