Population Health and Meaningful Use: Where is the patient?

The Meaningful Use criteria for implementation of electronic health record systems will be getting a new set of standards next year. MU offers $30 billion in subsidies to doctors who get their electronic systems up to speed… Previously, doctors have only had to prove installation and usage of EHR to get subsidies; now they need to implement Physician Quality Reporting System (PQRS) reporting. PQRS requirements are being pitched to doctors as helping them focus on patient outcomes. However, according to the new value model imposed by ACA, doctors will be reimbursed on outcomes demonstrated in PQRS reporting. Medicare reimbursement will be prorated at certain quality levels, rather than given simply for treating a patient, and many insurance companies have adopted value standards as well.

Population health metrics are important from a micro and macro perspective. Some doctors are already using population health. Most are either annoyed or confused by the transition, and don’t understand their patients any better after implementation. Doctors really don’t have time. Institutions are under-staffed. The imperative goes unfulfilled.

As information technology has evolved, it certainly became possible to analyze massive datasets, and “big data” is all the rage. In addition to the government, others are driving their understanding of patients through massive databases. Big pharma and health insurers are two examples. On the other hand, it is also becoming easier for computers to analyze “soft” data, like written texts,  images, or even facial expressions. IBM’s Watson famously relies on  unstructured data, and IBM is focusing a lot of energy on understanding the healthcare universe. Further out, sentic computing systems are being developed to understand, and respond to, the emotional state of the user.

The incredible growth in social media proves that it is already possible for people to develop and maintain emotionally satisfying relationships via computer interface. Most of the communities are heavily based in visual imagery as the primary vector for communication.

While it isn’t possible to replace physicians with computerized simulacra of them (but will this really always be true?), it could be possible to provide patients with a focused forum to engage in rich communication about health and illness. This forum doesn’t fulfill the role that physicians still occupy, but enhances it in a way that patients associate the online activities with the provider. This would be a real model of patient engagement.

BuzzBack has long focused efforts on integrating quantitative analysis with “softer” variables. How does the respondent (doctor or patient) feel and think when we let them play with pictures, or ask them to write a story? How do these creative and unconscious processes relate to a drug, disease or medical situation? Then, what happens when we quantify the results of these creative exercises across hundreds, or thousands of people? We have done work like this for pharmaceutical companies to understand how physicians and patients interact with, and feel about, each other. How well do they understand a product promise and what does it mean to them? There are many different contexts and situations where BuzzBack integrates image-based and story-based methods into quantitative studies. These help our clients get a deeper understanding of the customer.

EHR and PQRS certainly perform extremely important tasks. However, they don’t really help understand patient motivations and needs better unless system designers find ways to step outside the box of straightforward rational thinking. At BuzzBack we’ve been stepping outside the box for a while, understanding patient behavior in ways that matter. For example, we’ve looked at the emotions that drive dialog between patients and physicians in multiple therapeutic areas. Using pictures as a vector to communication of underlying feelings, we’ve looked at how doctors feel about medication treatments in a variety of disease categories. What works for them, what doesn’t, and why do they choose different drug brands? We’ve worked with both patients and doctors, asking them to tell stories about each other, or to imagine conversations about difficult situations. The results of these studies are usually pretty surprising. We access the kinds of things missing from how the industry at large looks at patients and physicians: as a collection of myriad data points. They “forgot” to build in the vital human qualities, but we’re trying to patch the gaps, piece by piece.