ENTRIES TAGGED "health IT"
Evolution from a research tool to a platform for patient engagement
Bruce Springer of OneHealth will speak about this topic at the Strata Rx conference. This article was written by Patrick Bane of OneHealth in coordination with Bruce Springer.
According to a recent study performed by the Jesse Brown VA Medical Center and University of Illinois at Chicago, patient-centered care has demonstrated positive outcomes on patients’ health, patients’ self-report of health, and reduced healthcare utilization. The study’s results are consistent with previous research that the patient-centered care model improves the quality of care while simultaneously lowering the cost of care.
OneHealth’s behavior change platform extends the patient-centered model by connecting members anytime, anywhere through mobile and web applications. Member generate data in their daily lives, outside of a clinical setting, which creates a much richer dataset of behaviors that are required to understand the patients’ condition(s), and their readiness to change. Members freely choose what to do and their choices actively generate data in five classes of information:
Data that matters to patients
This article is by guest author Amik Ahmad. He is speaking on this topic at Strata Rx.
Distractions didn’t have a chance. My phone was devoid of reception. The New York Times mobile application searched impossibly for a Wi-Fi connection. Conditions perfect for focus: away from a world always on and connected, noisy, and belligerent with information overload. I could have found joy in a single byte. But instead, I was pushed to the limit of sensory deprivation, and I teetered on the edge of insanity. I spent nine hours of my life in a hospital waiting room.
Hypothesis-free data analysis turns up unexpected incidences of illness
This posting was written by guest author Arijit Sengupta, CEO of BeyondCore. Arijit will speak at Strata Rx 2013 on the kinds of data analysis discussed in this post.
Much of the effort in health reform in the United States has gone toward recruiting 18-to-35 year olds into the insurance pool so that the US economy and insurers can afford the Affordable Care Act (ACA). The assumption here is that health care costs will be less for this young population than for other people, but is this true? Our recent analysis of 6.8 million insured young adults, across 200,000 variable combinations, suggests that young adults may be more expensive to insure than we realize.
Our study shows a high occurrence of mental health diseases among 18-to-35 year olds who have insurance and therefore more affordable access to medical care. Moreover, expenses associated with mental health conditions are very high, especially when coupled with a physical ailment. As the previously uninsured 18-to-35 year olds get access to affordable care, we may see a similarly high rate of mental health diagnoses among this population. The bad news is that the true costs of insuring 18-to-35 year olds might be much higher than previously suspected. The good news is that previously undiagnosed and untreated mental health conditions may now actually get diagnosed and treated, creating a significant societal benefit.
HealthTap refines the answers returned to specific health queries
HealthTap is a community of doctors and clients seeking answers to health questions. Its central service provides immediate access to doctors and their knowledge either by doctors answering client questions in real time, or through a large database of previously answered questions and answers from doctors that are peer reviewed and tagged with recommendations by other doctors. By combining the doctors’ recommendations with data provided by each client on himself or herself, HealthTap provides customized results to queries. In this video, HealthTap CEO Ron Gutman explains unexpected lessons they’ve learned from offering the intelligent search service.
A tool for outreach to patients produces unexpected benefits
The traditional, office-based model for health care is episodic. The provider-patient relationship exists almost completely within the walls of the exam room, with little or no follow-up between visits. Data is primarily episodic as well, based on blood pressure reading done at a specific time or surveys administered there and then, with little collected out of the office. And even the existing data collection tools—paper diaries or clunky meters—are focused more on storing data that on connecting the patient and provider through that data in real time.
There is no way to get in touch when, for instance, a patient’s blood sugar starts varying wildly or pain levels change. The provider often depends on the patient reaching out to them. And even when a provider does put into place an outreach protocol, it is usually very crude, based on a general approach to managing a population as opposed to an understanding of a patient. The end result is a system that, while doing its best within a difficult setting, is by default reactive instead of proactive.
Big Data and analytics are the foundation of personalized medicine
Despite considerable progress in prevention and treatment, cancer remains the second leading cause of death in the United States. Even with the $50 billion pharmaceutical companies spend on research and development every year, any given cancer drug is ineffective in 75% of the patients receiving it. Typically, oncologists start patients on the cheapest likely chemotherapy (or the one their formulary suggests first) and in the 75% likelihood of non-response, iterate with increasingly expensive drugs until they find one that works, or until the patient dies. This process is inefficient and expensive, and subjects patients to unnecessary side effects, as well as causing them to lose precious time in their fight against a progressive disease. The vision is to enable oncologists to prescribe the right chemical the first time–one that will kill the target cancer cells with the least collateral damage to the patient.
How data can improve cancer treatment
Big data is enabling a new understanding of the molecular biology of cancer. The focus has changed over the last 20 years from the location of the tumor in the body (e.g., breast, colon or blood), to the effect of the individual’s genetics, especially the genetics of that individual’s cancer cells, on her response to treatment and sensitivity to side effects. For example, researchers have to date identified four distinct cell genotypes of breast cancer; identifying the cancer genotype allows the oncologist to prescribe the most effective available drug first.
Herceptin, the first drug developed to target a particular cancer genotype (HER2), rapidly demonstrated both the promise and the limitations of this approach. (Among the limitations, HER2 is only one of four known and many unknown breast cancer genotypes, and treatment selects for populations of resistant cancer cells, so the cancer can return in a more virulent form.)
Increasingly available data spurs organizations to make analysis easier
Genomics is making headlines in both academia and the celebrity world. With intense media coverage of Angelina Jolie’s recent double mastectomy after genetic tests revealed that she was predisposed to breast cancer, genetic testing and genomics have been propelled to the front of many more minds.
In this new data field, companies are approaching the collection, analysis, and turning of data into usable information from a variety of angles.
Health data can go beyond the averages and first order patient characteristics to find long-term trends
This article was written with Arijit Sengupta, CEO of BeyondCore. Tim and Arijit will speak at Strata Rx 2013 on the topic of this post.
Current healthcare cost prevention efforts focus on the top 1% of highest risk patients. As care coordination efforts expand to a larger set of the patient population, the critical question is: If you’re a care manager, which patients should you offer additional care to at any given point in time? Our research shows that focusing on patients with the highest risk scores or highest current costs create suboptimal roadmaps. In this article we share an approach to predict patients whose costs are about to skyrocket, using a hypothesis-free micro-segmentation analysis. From there, working with physicians and care managers, we can formulate appropriate interventions.