Day 3 Notes on the 2018 JP Morgan Healthcare Conference
San Francisco (Wednesday, January 10, 2018) – The third afternoon of the conference was a deep dive into data and analytics, with successive presentations by IBM Watson Health, Inovalon and AthenaHealth and an earlier morning presentation by Chinese genomics company BGI. Significant progress was reported and, interestingly, the more these companies (and others like them) can do, the more that the market expands with new requests and newly discovered needs (a la “what can you really use a tablet for anyway?”). Inovalon reported that the total addressable market has grown in their estimation from $84 billion (as calculated back in 2014) to $142 billion as of 2018. Breaking that down, they believe that there currently is about $40 billion of demand from the provider sector, $51 billion from pharma/life sciences, $17 billion from payors and $33 billion from consumers.
What’s Necessary for Market Success in Big Data? – Quoting from the movie Top Gun, today’s providers and consumers feel the need, the need for speed. The legacy batch processing methodologies of old-style analytics may be falling to a real-time transactional processing model. What does that mean? Rather than doing periodic and inflexible data refreshes on a calendar that works for the Big Data company without regard to the consumer’s schedule, the objective is to have almost real-time updating of data…and the analytics that is derived from such data. Think of having each change in data almost simultaneously updating related insights, reports or interventions, all available on demand in real-time during a patient interaction or a provider decision-making process. That really does allow then a true consumer-centric or consumer-focused approach. To do that though requires provisioning of much larger compute and storage capacity and fast “pipes” such as fiber networks that can quickly move petabytes of data. Similarly, BGI, a Chinese genomics family of companies that has sequenced the human genome, the rice genome and the giant panda genome in the past twenty years, is working to provide whole human genome sequencing within 24 hours at an affordable price (currently, US $600 and projected to be US $300 by 2020). Beyond speed, there is the need for actionable intelligence with real world impact and effective data visualization and reporting. Put that together with large-scale data connectivity, data validation capabilities, and predictive analytics and now we’re talking!
There’s A Lot Going On – Inovalon is a cloud-enabled technology company with an integrated connectivity/analytics/high speed compute platform that serves health plans, providers, pharma and other healthcare organizations. Inovalon has increased from approximately 8 million patients on its cloud-native Big Data/analytics platform in 2015 to 94 million in 2017. Reported clients include 19 of the top 25 U.S. health plans and 13 of the top 15 pharma/life sciences companies. Similarly, IBM Watson Health reported that it now has 210 million clinical and claims records in its system and continues to roll out new products, such as dashboards for better case management by juvenile courts and social workers, annotators to add medical insights from full reviews of all patient records and data correlated with full scientific literature reviews, correct coding and suggest better differential diagnoses and applicable treatment pathways. Their drug discovery product also is suggesting genetic associations not previously known, such as identifying 5 new proteins not previously linked with the ALS disease.
Fun Facts from Today’s Big Data Fest – Did you know that August is the month in which more Chinese women become pregnant than any other month of the year (May and July also are very popular). Thanks to BGI for that interesting tidbit, which was derived from their analysis of non-invasive pre-natal testing (NIPT), a blood test that screens for genetic conditions. Moving to the doctor side of the table, AthenaHealth shared some of its analysis of its AthenaNet data for the physicians on their platform. Per AthenaHealth, independent physicians have 26% more appointments per year than large health system physicians. Also, even with increasing access to care being the focus of many efforts currently, AthenaHealth found from their analytics that 27% of physician appointment calendars nationally go unfilled. To combat this, Athena is working on an OpenTable type of universal physician booking system to reserve appointments, both for AthenaHealth and non-AthenaHealth physicians. An interesting concept and, frankly, one that leads to the question as to why the healthcare industry doesn’t practice yield optimization like airlines and hotels do? While we certainly would like to have lower prices for unsold, last minute medical appointments and procedures (per AthenaHealth, medical equipment sits unused for 58% of the time), think about the other side of the equation – do we really want to have “surge” pricing take hold in healthcare, like we see with Uber? That said, market elastic pricing does reflect value and demand, but perhaps we can get to the same result more kindly by applying Big Data and predictive analytics to forecast adverse health “surges” and try to mitigate them or to reduce their incidence if possible through preventive care, disease management and lifestyle changes (i.e., population health management tactics that are indeed effective). And the joy and the burden of a large, diverse healthcare system is that we don’t have to have “one size fits all,” as different sectors and companies can experiment with varying approaches.
That was Then, This is Now – As an interesting note on the healthcare system, Jonathan Bush of AthenaHealth noted that there has been a 38% reduction in hospital beds in the U.S. since the 1970s, as well as a 21% decrease in the number of hospitals. But even with that, the 1970 occupancy rate of 76.7% has further declined to 64.7% as of 2014. (See also my earlier note this week regarding the current softer volume trends for hospitals) We all can – and do – extrapolate on the impact of growing outpatient treatment site choices on hospital bed occupancy, but, given today’s Note topic, what will be the effect of truly effective Big Data analytics on hospital occupancy, revenue and profitability? Will we catch more disease earlier and increase volume as we identify more disease, or will real-time transactional, artificial intelligence enabled analytics result in a “bending of the disease curve” and further accelerate the decline in inpatient utilization. And if such is the case, revisiting the above pricing discussion, does inpatient care pricing get even more expensive then as lower volume requires higher pricing to cover the expense “nut” of keeping the hospital doors open and the facility capable? We have not really begun to effectively think through the cost and pricing impacts of Big Data and artificial intelligence, but we as an industry should as we plan for the interesting changes to come in the next decade.
Quote of the Day – “Healthcare is the only industry where you can say the same thing for ten years and still be seen as a visionary.” (Jonathan Bush, AthenaHealth)