Achieving the Quintuple Aim - A Focus on Improving Health Outcomes

By Milissa Campbell

If you have been following our series on Data-Driven Achievement of the Quintuple Aim, you will remember that we started our exploration of the five aims with an emphasis on Clinical Professional Experience and Health Equity. Prioritizing these two systemic needs will get the patients with healthcare needs access to a healthcare system where they are met by a clinical workforce that is supported and enabled.? If we achieve this, outcomes will improve, costs will go down, and the patient experience will be transformed.? Today, we will discuss the third aim of our journey, a data-driven approach to improving health outcomes:

The U.S. has the lowest life expectancy at birth, the highest death rates for avoidable or treatable conditions, the highest maternal and infant mortality, and some of the highest suicide rates among high-income nations [1]. In addition, our life expectancy is declining while investment in our healthcare system increases. While our system is a complex symphony of drivers and dependencies, and there is not a single silver bullet, the key to understanding and reversing these trends is - you guessed it - data.

Drivers of poor health outcomes can be divided into three general buckets: An individual’s healthcare behavior, access to care, and quality of care. Given that there is a lot of information about the quality of and access to care, I will not cover those in depth here.? However, let’s discuss leveraging data to understand and change healthcare behaviors to support improved outcomes:

Healthcare Behaviors are the choices we make every day about our bodies and health that impact our overall well-being. When I meet with healthcare companies, this is the most often overlooked area of analysis. Access and quality are analyzed frequently, but predicting those behaviors and choices that affect your health is a gap. How we make health choices can be analyzed using the same techniques that other industries use to predict whether we will buy Coke or Pepsi, whether we will pay our credit card, or whether we are likely to respond to upsell efforts. The variables may differ, but we leverage the same approach.? Consider these data-driven approaches:

• Adding robust consumer data to your clinical data and leveraging propensity models to quantify a patient’s likelihood to engage or complete a program, to readmit, or to attend follow-up care.
• Looking for patterns in your pharmacy fill data to identify specific causes of non-adherence and target them with specific interventions, as not all low adherence is due to forgetting to take medications. For example, patients who fill their 30-day prescriptions consistently every 60 days and maintain a consistent 50% compliance could be “pill splitters” who are attempting to save money by cutting pills in half and stretching their refills.

Similarly, Access to Care includes the following areas of measurement:

• The location of hospitals and appropriate services
• The availability of clinical professionals
• The cost and coverage of care
• Healthcare literacy
• Language
• Transportation

Some highlighted examples are:

• Using Geo Access software to move beyond a macro view of hospital analytics to patient location analytics. This includes enhancing internal data sets with public and subscription geographical data to evaluate access to key specialists and clinicians of the same gender, culture, and ethnicity as those they serve.
• Using retailer data to understand when and where people who look and act like your population shop and when they do.? As part of this, you should evaluate whether you can provide pop-up or mobile services to those areas with the greatest access needs and meet patients where they are.
• If it is not known whether a patient has a transportation barrier, the U.S. Department of Transportation has a plethora of public data sets that can be used to calculate a person’s risk of having a transportation barrier.? Clinicians who are aware of high-risk barriers are better positioned to mitigate them.

Quality of Care is a bit more intuitive to define with some rich opportunities for using data to make a difference.? Quality of care measurement includes things like:

• Improved health vitals (BP, A1c, weight, BMI, etc.) as well as the sustainability of the improvement
• Reduced or mitigated disease state
• Provision of preventive care
• Screening rates for appropriate co-morbidities
• Appropriate utilization of higher levels of care
• Reduced or mitigated adverse events
• Utilization of services and levels of care
• Care/Case/Disease Management impact
• Prescribing Patterns/Appropriateness

Examples of using data well in this space include:

• Using data sets beyond clinical data (geography, consumer, shopping, employment, etc.) to build risk models that identify potential for disease onset or advancement, ED use, screening rate compliance, etc.
• In addition to using external data, ensuring that relevant clinical data is being collected in the right way, at the appropriate time and frequency.? I recently spoke with a colleague who indicated an internal effort to revisit the correct way to take a patient’s BP after observing significant variations from clinical guidelines.? It is this kind of foundational activity that can feel burdensome and unnecessary but ensures that the quality of the data is at its highest level.

In closing, let me leave you with a reminder of the basics, to truly improve health outcomes through behavior, access, or quality, you must:

• Curate broad diverse data sets both internal and external to the organization
• Ensure data quality through appropriate governance and control mechanisms
• Ensure security, privacy, and transparency of your data
• Be militant in your pursuit to remove bias from your data and analytics
• Stop asking limited questions of your data, let the data show you the patterns and themes with the greatest value and power.? If you limit your data mining to your specific questions, then you limit your learnings and impact to what you know and think.

Stay tuned for our fifth installment in this series, diving deeper into the Reducing Cost component of the Quintuple Aim.

Do you need help building your data foundation, defining your data-driven journey, or building your data-driven solutions? Send us a message to discuss how Excell HCA can accelerate your Quintuple Aim Journey - we will make a difference for your organization.

Footnotes

[1] https://www.commonwealthfund.org/publications/issue-briefs/2023/jan/us-health-care-global-perspective-2022#:~:text=The%20U.S.%20has%20the%20lowest,nearly%20twice%20the%20OECD%20average