ENTRIES TAGGED "health care"
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.
Arijit Sengupta of BeyondCore uncovers hidden relationships in public health data
The importance of visualizing data is universally recognized. But, usually the data is passive input to some visualization tool and the users have to specify the precise graph they want to visualize. BeyondCore simplifies this process by automatically evaluating millions of variable combinations to determine which graphs are the most interesting, and then highlights these to users. In essence, BeyondCore automatically tells us the right questions to ask of our data.
In this video, Arijit Sengupta, CEO of BeyondCore, describes how public health data can be analyzed in real-time to discover anomalies and other intriguing relationships, making them readily accessible even to viewers without a statistical background. Arijit will be speaking at Strata Rx 2013 with Tim Darling of Objective Health, a McKinsey Solution for Healthcare Providers, on the topic of this post.
Donald Berwick discusses health care improvement: goals, exemplary organizations,and being at a turning point
A video interview with entrepreneur Colin Hill
Last week, a wide-ranging interview on data in health care took place between Dr. Donald Berwick and Colin Hill of GNS Healthcare. Dr. Berwick and Hill got together in the Cambridge, Mass. office of the Institute for Healthcare Improvement, a health care reform organization founded by Dr. Berwick, to discuss data issues related to O’Reilly’s upcoming Strata Rx conference.
Berwick returned to IHI after his year as administrator of Centers for Medicare & Medicaid Services. Throughout these changes he has maintained his stalwart advocacy for better patient care, a campaign that has always been based on a society’s and a profession’s moral responsibility. Even an IHI course for the “Patient Safety Executive” program puts “Building a just culture” on its agenda.
Among the topics Berwick and and Hill look at in these videos are the importance of transparency or “turning on the lights,” ways of learning from the health provider system itself as well as from clinical trials, types of personalized medicine, the impediments to collecting useful data that can improve care, exemplary organizations that deliver better healthcare, and how long change will take.
The full video appears below.
Report from OpenClinica conference
Although open source has not conquered the lucrative market for electronic health records (EHRs) used by hospital systems and increasingly by doctors, it is making strides in many other important areas of health care. One example is clinical research, as evidenced by OpenClinica in field of Electronic Data Capture (EDC) and LabKey for data integration. Last week I attended a conference for people who use OpenClinica in their research or want to make their software work with it.
At any one time, hundreds of thousands of clinical trials are going on around the world, many listed on an FDA site. Many are low-budget and would be reduced to using Excel spreadsheets to store data if they didn’t have the Community edition of OpenClinica. Like most companies with open-source products, OpenClinica uses the “open core” model of an open Community edition and proprietary enhancements in an Enterprise edition. There are about 1200 OpenClinica installations around the world, although estimation is always hard to do with open source projects.
What is Electronic Data Capture? As the technologically archaic name indicates, the concept goes back to the 1970s and refers simply to the storage of data about patients and their clinical trials in a database. It has traditionally been useful for reporting results to funders, audit trails, printing in various formats, and similar tasks in data tracking.
Report from 2013 Health Privacy Summit
The timing was superb for last week’s Health Privacy Summit, held on June 5 and 6 in Washington, DC. First, it immediately followed the 2000-strong Health Data Forum (Health Datapalooza), where concern for patients rights came up repeatedly. Secondly, scandals about US government spying were breaking out and providing a good backdrop for talking about protection our most sensitive personal information–our health data.
The health privacy summit, now in its third year, provides a crucial spotlight on the worries patients and their doctors have about their data. Did you know that two out of three doctors (and probably more–this statistic cites just the ones who admit to it on a survey) have left data out of a patient’s record upon the patient’s request? I have found that the summit reveals the most sophisticated and realistic assessment of data protection in health care available, which is why I look forward to it each year. (I’m also on the planning committee for the summit.) For instance, it took a harder look than most observers at how health care would be affected by patient access to data, and the practice of sharing selected subsets of data, called segmentation.
What effect would patient access have?
An odd perceptual discontinuity exists around patient access to health records. If you go to your doctor and ask to see your records, chances are you will be turned down outright or forced to go through expensive and frustrating magical passes. One wouldn’t know that HIPAA explicitly required doctors long ago to give patients their data, or that the most recent meaningful use rules from the Department of Health and Human Services require doctors to let patients view, download, and transmit their information within four business days of its addition to the record.
A network graph approach to modeling the health-care system.
To achieve the the triple aim in healthcare (better, cheaper, and safer), we are going to need intensive monitoring and measurement of specific doctors, hospitals, labs and countless other clinical professionals and clinical organizations. We need specific data and specific doctors.
In 1979 a Federal judge in Florida sided with the AMA to prevent these kinds of provider-specific data sets violated doctor privacy. Last Friday, a different Florida judge reversed the 1979 injunction, allowing provider identified data to be released from CMS under FOIA requests. Even without this tremendous victory for the Wall Street Journal, there was already a shift away from aggregation studies in healthcare towards using Big Data methods on specific doctors to improve healthcare. This critical shift will allow us to determine which doctors are doing the best job, and which are doing the worst. We can target struggling doctors to help improve care, and we can also target the best doctors, so that we can learn new best practices in healthcare.
Evidence-based medicine must be targeted to handle specific clinical contexts. The only really open questions to decide are “how much data should we relese” and “should this be done in secret or in the open.” I submit that the targeting should be done at the individual and team level, and that this must be an open process. We need to start tracking the performance and clinical decisions of specific doctors working with other specific doctors, in a way that allows for public scrutiny. We need to release uncomfortably personal data about specific physicians and evaluate that data in a fair manner, without sparking a witch-hunt. And whether you agree with this approach or not, it’s already underway. The overturning of this court case will only open the flood gates further. Read more…