How healthcare organizations can maximize their return on insights

Like all commercial enterprises, healthcare organizations need to make a return on their investments. In recent years, we’ve seen how data has helped them achieve this by revealing costly inefficiencies and broken processes. But while many providers have seen success here, few have been able to tap into the vast potential that a special kind of ROI – return on insights – can offer when it’s done at scale. But what does it mean to go beyond data and harness the power of insights at scale, and what new returns can healthcare leaders expect to generate?

For the purposes of this post, we won’t dive into the weeds of insights-at-scale infrastructure – more on that in a future post. Instead, let’s focus on what I find most exciting about insights at scale: benefits healthcare leaders should embrace because of the compounding value that they can generate.

Let’s look at an example. Nearly every ambulatory care clinic has to navigate the delicate balance between variable-length appointment times, late arrivals, last-minute bookings, no-shows and staff absenteeism. Managing these variables manually is inefficient, costly and a burden on your teams. So you invest in a digital solution – an AI system that analyzes data on your patients and assigns a no-show prediction to each of them. The system has just returned the following insight: “Sara has an 80% no-show likelihood for her next appointment.” While that insight is simple and self-explanatory, there is a compounding value it can generate for your organization:

• Initial reaction to the no-show prediction may be to double book Sara’s time slot. After all, ensuring your appointment is full generates the greatest value for both your organization, and for the “other patient” that benefits from the likely open slot. This seems like a win-win – except perhaps for Sara. Because if Sara does show up, and her slot is double booked, she may potentially have less clinician time allocated to her.
  
• The compounding positive effect on the organization begins when your teams asks, “Well, why is Sara not likely to show up? Does she need a babysitter? Does she have transportation issues? Are there other insecurities that are predicating her to missing her appointment?” This is a second-order positive effect, as it drives your organization to perform more targeted outbound reach-out to support Sara and ensure she can make her appointment.
  
• Moreover, many of these outbound reach-outs can be automated using technology to bring scale to your organization, improving care delivery even at reduced cost. In fact, using these methods alone we’ve helped our partners reduce no-shows by up to 45% – not only improving delivery for those scheduled (since they didn’t miss their appointment) but also reducing empty appointment slots or double bookings, which reduce the patient experience.
• Finally, having established a preferred line of communication with Sara and other potential no-show patients, your organization can then take the opportunity to strengthen the patient-provider relationship. This allows a more refined approach to potentially vulnerable individuals to better support their care journey. For example, you could enroll Sara to a patient-engagement initiative that provides educational resources about managing her condition(s), or encourage her to join a patient portal where she can easily document her symptoms and treatment progress from home, increasing your oversight of her when she’s not in the clinic, as well as improving her access to care. (We recently launched such a portal to help strengthen patients’ knowledge and satisfaction after functional endoscopic sinus surgery (FESS) using Philips Patient Management Solution – this led to a significant improvement in knowledge of their medical conditions, increasing their ability to play an active role in their care management.)

Consider where we started (with a single operational insight) versus where we’ve ended up: that isolated 80% prediction unlocked a broader set of value; this is your Return on Insights. This is the power of insights at scale: it’s not always about having a large number of atomic insights – it’s about the second-order effects generated by those insights, leading to high-value, far-reaching returns across your organization.

To put these compounding returns in a real-world context, we recently collaborated with West Moreton Hospital in Australia, which was looking for a way to reduce in-hospital COVID-19 transmissions and the associated burden on their teams. We developed a remote-care solution where patients were given tablets and medical devices to measure and report just three values: temperature, blood pressure and oxygen levels. The data generated was then fed into an integrated telemonitoring platform that returned insights on likely patient deteriorations – prompting clinicians to intervene earlier, or start dialogues with the most at-risk patients, increasing patients’ ability to self-care from home. The insights generated resulted in a 53% reduction in emergency department visits, savings of AUD 1,010 per patient per month, and a 28% decrease in potentially preventable hospitalizations in chronically ill patients – all while simplifying the clinician experience and handing more independence back to patients. Once again, we see how even a relatively low insight ‘set’ can generate a multitude of high-value returns.