A Vision for Consumer Engagement in Health 

Rebecca Nielsen, Managing Director, Leavitt Partners, an HMA Company
Spencer Morrison, Associate Principal, Leavitt Partners, an HMA Company 

PillPack boasts the tagline, “Pharmacy Simplified.” The mail-order pharmacy successfully streamlined the activities associated with managing medications. It illuminated for its customers what medications they needed to take next, and at what time, by packaging meds in a disposable dose packet, printed with the date and time the dose should be taken. Joe Flaherty of Wired magazine observed, “A jumble of amber bottles are replaced by an efficient to-do list made of drugs.”1 

The PillPack solution is emblematic of a larger solution that is needed in healthcare. PillPack customers understood the next dose they needed by referencing the next packet in their PillPack box. Similarly, the time is ripe for providers, supported by a robust technology platform, to systematically deliver for consumers the next best step they should take to maintain or improve their health. 

The solution we envision would harness the various sets of data requisite for illuminating and then rank-ordering steps, and then, like PillPack’s dose packets, make those actions easily accomplished—and transactional—so that with a click or two, consumers can initiate, or even complete, a step. Each step completed would yield positive feedback. 

This gamified interface for taking the next best step could be a widget, an app, or a web-based portal.  

For example, if an otherwise healthy 47-year-old woman at average risk for colon cancer opened the app, it might indicate that her “next best step” to maintain or improve her health would be to choose a colorectal cancer screening method. It could present options (such as a colonoscopy, a fecal immunochemical test, or a DNA test) along with the associated out-of-pocket cost based on her specific benefit plan.2 It might rank-order the options based on the test she is most likely to take (as a single mother with a limited income, a home test may be most practical).  

Upon choosing a screening method, her “next best step” appears. It might be to schedule or order a screening. If she selects a home test, she will have the option to simply place the order via the app interface. After placing the order, the “next best step” might be something totally unrelated—such as taking her multivitamin—until the home test arrives, at which time the “next best step” might notify her to complete the home test. After she takes it, the “next best step” might be to send it to the lab, and so on. 

If engaging in one’s healthcare were distilled to taking discrete, simple, sequential, evidence-based and rank-ordered actions, people would likely be more engaged in their own healthcare decisions. We find consumers engaging readily in other areas of their lives when even complex information is presented in this manner.3  

The data and technology that undergirds this type of interface is both intuitive and extensive. In the example we have provided, the platform would need to access demographic, social, benefit, clinical (i.e., EHR) and pharmacy data, in addition to evidence-based rules and guidelines and scheduling and ordering functionality. One could imagine that in support of other examples, the platform also would need laboratory data, self-reported data, and claims data. The consumer interface would enable the user to add new health information (e.g., disease symptoms), and the next steps would adjust in response to this information. For example, “Try self-care for your insect bite” may supersede “Choose a colorectal cancer screening method.” 

Although we have discussed the consumer interface to this point, we envision a provider interface as well, whereby the provider could inform and/or input information to highlight the patient’s next best step. Next steps derived from evidence-based guidelines would appear in one color. Next steps tailored or informed by the individuals’ provider would appear in a different color to distinguish between recommendations derived from the platform’s data (e.g., adults older than age 45 should be screened for colon cancer) and tailored guidance from a licensed physician (e.g., given the consumer’s unique risk factors, the physician recommends colonoscopy over other test modalities). 

Electronic health records traditionally have been repositories for clinical documentation. That clinical documentation has informed provider decision making and has been provided in limited forms to other institutional stakeholders, but like a stagnant saltwater lake, its data rarely flow to the consumer. What would enable our vision for consumer engagement in healthcare is something more akin to a customer relationship management system (CRM), where clinical, administrative, benefit, and other data are input, stored, and linked with the express intent of enhancing patient engagement. However, the data in this repository are not delivered to consumers as a cumbersome patient health record. The data, or rather insights derived from a synthesis of disparate data streams, flows to the consumer as distilled, actionable, and sequential steps. 

A key principle behind this vision is that individuals, with appropriate context, can and should be an agent in their own healthcare. The ultimate end of all healthcare-related data, whether it is clinical or administrative, should be to enlighten and inform people’s understanding of their health and support their healthcare decision-making. Another key principle is that laypeople should not be expected to combine, distill, and interpret raw data sets into actionable insights. Rather, healthcare professionals should provide and increasingly synthesize, frame, and present relevant data so that their patients can make optimal decisions. 

With that contextual information, the woman in our earlier example would understand her colon screening options. If she chooses, she can “swipe left” to reject or postpone the recommended next best action, but the disparate sets of healthcare data will have been provided to help her to make a streamlined and evidenced-based decision that is right for her. 

This vision also paints a picture of the optimal relationship between consumers, their providers, and technology, in which the benefits of the patient-provider dyad are leveraged but in the context of a digital interface that harnesses the full powers of the convenience and accessibility that technology affords. Our vision for the future is a triad between the consumer, her provider, and technology (encompassing relevant data, evidence-based guidelines, and functionality to support action).  

Consumers are already searching the internet when health issues arise. Karen De Salvo, MD, in Google’s The Check Up ‘23, noted that “three out of four people turn to the Internet before they seek care,” and said “people will expect a mobile-first experience.” This vision ensures that providers are not disintermediated from healthcare decision making, but rather that they meet consumers where they already are. 

Some providers and insurers lament that people do not engage more readily in healthcare. We believe that when healthcare stakeholders deliver evidence-based “next best steps” in a distilled, contextualized, and sequential format that leverages clinical, administrative, and social data streams, and functionality to enable taking action, such as ordering and scheduling, people will engage. And in the process, they will achieve better health. 

  1. Flaherty J. A Drug-Dealing Robot That Upends the Pharmacy Model. Wired. February 14, 2014. Available at:,A%20jumble%20of%20amber%20bottles%20are%20replaced%20by%20an%20efficient,do%20list%20made%20of%20drugs.&text=Most%20designers%20can%20kvetch%20about,and%20regulations%20that%20govern%20pharmacies. Accessed September 15, 2023. ↩︎
  2. Risk-bearing providers may implement value-based insurance design that incentivizes these same high-value activities. ↩︎
  3. Duolingo, which distills the daunting and complex task of learning a new language into sequential steps has achieved surprisingly high levels of adoption. ↩︎