In only its second year, HLTH is a brilliantly marketed and rapidly growing conference (think mini-HIMSS meets Exponential Medicine) that brings together a high-quality collection of digital health startups and incubators, large company health teams, heads of innovation at providers and payers, and investors. For a young conference, it pulled a surprising number of big-name speakers like Seema Verma and Mark Cuban and countless CEOs. With 6,200 attendees it has enough critical mass (everyone is there) without being overwhelming.
The sessions hit the full spectrum of interesting topics in the ever-expanding world of digital health. While each session tended to be more an amuse-bouche than a full meal, there were plenty of nuggets across the three and a half days, especially addressing patient engagement.
Like the need to meet providers in their workflow, we need to meet patients in their lifeflow.
It’s amazing how different we all are, and we keep discovering more ways that each one us is unique, such as voice-print and muscle-print (how we move). The magic of creating more value for patients comes when you meet them in the unique place they are. It can be as simple as communicating via text instead with a phone call. In one instance of collecting survey responses, for example, participation went up from 25% to 70% by switching the administration method from phone call to text. It’s important, therefore, to consider engaging patients in the medium where they are most comfortable. Are they commonly engaging with texts, emails, phone calls or something else?
Moreover, it’s time to stop stereotyping communications preferences. Nearly everyone has a smartphone and data access, including 72% of homeless in one cited study, and “All our Medicaid patients are on Facebook,” quipped a health system executive. That doesn’t mean we don’t need to provide access solutions for all scenarios, but it’s definitely not accurate to say old people aren’t online.
Different ages, languages and cultures can be indicative of how a patient will prefer to interact with healthcare providers and manufacturers. However, it’s difficult to optimize programs and solutions for these differences without combining the best of artificial intelligence (AI)–natural language processing (NLP) and machine learning (ML)–and humans.
To complicate things further, it’s important to realize that patient preferences aren’t the same from day to day. Are they a caregiver or a patient? Confused, tired or stressed? Or is everything upbeat? When feelings can change from day to day, content and tone should be altered to address different emotions and demographics. Though they are technically the same patient, the way we address someone who has been newly diagnosed and someone who has already been on therapy for some time will be drastically different.
Every technology needs talent to deliver a 100% solution.
We’ve been saying this for a while, and in healthcare it’s especially true. You can’t deliver a minimum-viable product when people’s health is at stake. Especially when a therapy is new or the patient population is small, there are challenges to delivering a product that fully addresses their needs. Even when the technology serving you is perfect, there are still piles of paper, fax machines and interoperability challenges, which means you automate processes as far you can and get to 100% with talent.
Looking to the future, AI in partnership with humans is the pathway to scalable, quality healthcare. It’s still overhyped, but it’s rapidly being refined to work in real-world applications to improve efficiency and outcomes. The explosion of available data, plus the ability to analyze it consistently, delivers new insights that can be leveraged to improve quality for patients and providers.
Companies are learning how to localize and build in contextual understanding in machine learning (ML) and natural language processing (NLP) algorithms. In terms of practical applications, NLP can be leveraged to scan call transcripts to determine which patients need escalation and coaching opportunities for agents. To keep improving NLP, companies must determine where the conversations are stopping and ask patients to evaluate the interactions. Both ML and NLP technologies can only improve with the input of your human talent.
We’re still paying for sickness ($3.7T), not wellness ($1B), but there are signs of progress
For reimbursement and traction, Wellness 2.0 initiatives must provide ROI via metrics, outcomes and scientific studies. The Randomized Control Trial is still the gold standard for proving you can deliver results and smart startups are increasingly making sure they include these in their plans. Even with evidence, payers want to see per member, per month (PMPM) cost reductions to consider reimbursement.
Payers, providers and startups are exploring the potential to use social determinants of health (SDOH) and lifestyle interventions to improve outcomes with some promising results. Geisinger reported that its expanding Fresh Food Farmacy program was delivering better results than medicine. Virta Health shared its successes with long-term reversal of type 2 diabetes with ketogenic diets. Cityblock is demonstrating success with holistic care for the sickest Medicaid patients. In order to get the most from wellness initiatives, anti-kickback regulations need to change to enable legitimate and appropriate provider and pharma-led lifestyle and SDOH programs that include items that could be considered of monetary value.
Further, health systems are moving from experimenting to deploying telehealth, remote monitoring and patient engagement to extend care outside of the hospital enabled by reimbursement (new CPT codes), and tech (lowering costs, miniaturization and battery life). As healthcare preferences shift with new ways of providing healthcare and health information, we’ll begin to see the amount we’re paying for sickness decrease with increased efficiencies.
These efficiencies are empowered by patient access to data, and patient power will increase with more direct-to-consumer options, greater access to information and perhaps even control of their own health information. Patients empowered with access to their own data and partnership with the care team have improved adherence and outcomes.
Patients don’t always want to be engaged, no matter how good the offer.
“Often what you think will be easy is hard and vice versa.” Hank Schlissberg from Vively Health shared how very sick patients would say no to their offer of providing free health care in their homes. Finding the right patients is hard. Getting them to say yes is even harder. Cityblock recounted that it could take five or six in-person chats to secure enrollment. Consumer companies and behavioral economics may provide clues on how to do it better, but one adage remains true: “Trust is hard to get and nurture, and easy to break.”
Patients want help managing their disease without being defined by it. We should remember that patients are consumers that got sick and that we will all be patients at some point.
Healthcare is unique in its obscurity of cost and quality.
There are challenges with predicting what services will be rendered, what they will cost, and what quality will be delivered. The lack of clarity, discoverability and transparency reduces the ability for patients to make informed choices.
Patients are scared to get started with care for all sorts of reasons. Many don’t have a primary care physician (by some studies >50% of millennials) and don’t know where to start. Moreover, they don’t know if they go into care if they can afford it. When it’s difficult to assess what services are needed and at what cost, it’s necessary to lean on talent to fill in the knowledge gaps. Experts with years of experience working with payers, patients, manufacturers and pharmacies can take the patient journey from one of ambiguity to a positive consumer experience.
The HLTH conference made abundantly clear what we here at AssistRx already know, that the perfect blend of technology and talent is needed to improve patient lives and speed their access to therapy. When technology takes over the automatable portions of the healthcare system, and the intricacies are addressed by talent who are knowledgeable in the industry, we come much closer to the perfect healthcare model.
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