When Worlds Collide: Patient Advocates and Health Plans Unite on Comparative Effectiveness
Posted by Richard Noffsinger in Comparative Effectiveness on May 18th, 2009
There’s an overwhelming wealth of information to help improve health care outcomes, with often only primitive means for accessing that information.
The chasm is slowly closing between patients’ rights advocates and health plans: insurers are using their heft to influence measures that proactively improve health care quality and lower costs. Patient groups may agree that things are moving in the right direction, but conflicts still exist. One emerging area of health care policy, however, has generated instant alignment among the two often divided groups — the pursuit and utilization of comparative effectiveness research, according to a recent op-ed piece in the Baltimore Sun,
Comparative effectiveness research will gather information on treatments and related outcomes for chronic conditions to create a database of evidence-based intelligence. Physicians can leverage this intelligence to increase the likelihood of positive outcomes and avoid treatments that don’t work.
Concerns have been raised that this approach will lead to cookie-cutter solutions to complex health issues, but that’s not the case. Sophisticated clinical decision support (CDS) systems exist right now to analyze multiple information sources, such as a patient’s medical history and other clinical data, to suggest highly personalized treatment recommendations. Interoperability with comparative effectiveness data as a rules source can add a new, powerful dimension to the analysis and generation of treatment options.
But there’s one major obstacle. As I’ve often seen, it’s not a dearth of information that prevents the incorporation of evidence-based data into medical decision making, but rather it’s an overwhelming wealth of information with often only primitive means for accessing that information. It’s tougher than you think to standardize this information into a single format accessible enough to seamlessly assist with decision making. It takes a high-performance analytics engine with superior thesaurus-like data translation capabilities.
When considering health-care analytics solutions, it’s important to evaluate interoperability with multiple data sources along with the flexibility to add new data sources, such as comparative effectiveness, as they become available. Only CDS systems that can adapt and grow with the rapidly evolving health information landscape will serve patients – and health care providers — best in the long run.
# # #
Richard Noffsinger is CEO of Anvita Health.
Health Consumerism + Analytics: Power to the Patient!
Posted by J. Matt Yuill, M.D. in Health Consumerism on May 15th, 2009
Easier to ask about a side dish than side effects
As a physician informaticist who still sees patients part time, I like my patients to be actively involved in making medical and treatment decisions. I find that those patients who ask questions and get involved in decision making tend to be more satisfied with their care. An informed patient tends to be an adherent patient and that’s a win-win situation.
If the Agency for Healthcare Research and Quality (AHRQ) has anything to do with it, this trend toward health consumerism will kick into high gear as a result of an ad campaign it recently launched to encourage consumers to ask questions in their doctor visits. The campaign shows how easy it is to ask a waiter about a side dish compared to asking a physician about a medicine’s side effects, according to an article in the Washington Post last week, and helps consumers learn how to ask the tough questions.
I salute this promotion of health consumerism, or increased patient self-advocacy. It puts a spotlight on the need for more personalized patient visits, and gives the provider valuable input from the patient on their personal health priorities — information that might not come from any other data source.
Wearing my informaticist hat, I embrace the value of personalized information to achieve better quality health care. There is so much individual patient data that can be leveraged for advanced clinical decision support (CDS). Conversely, the recommendations from CDS should be viewed in the context of the patient’s individual health status and health goals.
An example: A patient of mine named Phil has diabetes. Standard rules-based CDS tools have the tendency to treat Phil as “a noncompliant diabetic” because he and I have agreed to check his Hemoglobin A1c every six months due to costs, but the prefabricated alerts label him noncompliant if a test is not performed every three to four months. Phil is a unique individual with unique lifestyle conditions, a unique medical history, unique conditions that influence his adherence to medication therapies and his ability to comply with guidelines, and so on. While all of this information is critical in the diagnosis and treatment of Phil’s health issues, it may be disregarded by clinical decision support that only evaluates a few factors based on historical information.
Advanced real-time CDS tools, however, are able to take in data from a multitude of sources, including information Phil shares with his physician, care manager, or even pharmacist, and deliver updated treatment recommendations in under a second. Comprehensive data sets that incorporate new information at the point of care more accurately define Phil’s individual health status and give more actionable intelligence about to how best treat Phil’s health issues.
The desire for personalized health care is nothing new, but with new advanced clinical data analytics options, that personalized experience can translate into measurably better health care quality.
# # #
J. Matt Yuill, M.D., CPC, is board certified in internal medicine and pediatrics, and earned his doctor of medicine degree from the University of Kentucky College of Medicine in 2000. Dr. Yuill is also the founder of the HCC Blog and is a nationally recognized expert on Medicare Advantage risk adjustment. Dr. Yuill is a certified professional coder, and joined Anvita Health as a physician informaticist in 2008.
Physicians as Pharmacists – When it Makes Sense
Posted by Richard Noffsinger in Analytics and Health Care Providers on May 8th, 2009
Some of the nation’s largest pharmacy benefit management companies have made public their intentions to improve adherence by leveraging available patient clinical data to help patients improve adherence to medication therapies for better outcomes and lower overall health care costs. Under this banner, a pharmacist can be empowered to intervene with patients who have stopped taking medications or who take them less frequently than originally prescribed. This personalized information delivered by a trusted health care provider at a time when the patient is in a teachable moment has and will continue to lead to effective interventions.
But how do you intervene on a patient that never makes it to the pharmacy? A study published last week in the Archives of Internal Medicine reported that high copayments deter patients with newly diagnosed chronic conditions from initiating their drug therapy, especially if they have little experience with taking medications.
While the study suggests that lowering co-payments may encourage patients to begin and adhere to drug therapy, I see a big opportunity to inject more value into the physician interaction by arming them with timely, relevant, patient-specific information at the time of prescribing that can minimize the co-payment obstacle and improve adherence.
Using advanced real-time clinical decision support coupled with e-prescribing, when appropriate, a physician can identify safety- and formulary-checked therapeutic alternatives that have a lower co-pay to encourage the patient to start their drug therapy right away. The thought is not to place additional burden on the physician whose day is already taxed with patient visits and administrative work. By building the advanced analytics capability into the e-prescribing application, the work is done for them.
We all know about alert fatigue with e-prescribing and the rate at which alerts are overridden, and what I’m talking about is not an alert. It’s just simply providing the physician with drug options driven by actionable health intelligence derived from patient data all with the intention of improving the likelihood of adherence. This is clinical decision support at its best.
As CMS shapes and enforces its criteria for e-prescribing incentives, it makes sense to give weight to any mechanisms proven to improve adherence to drug therapy.
# # #
EMRs and the Stimulus Package – What About Analytics?
Posted by Richard Noffsinger in Policy on May 6th, 2009
Richard Noffsinger is CEO of Anvita Health
Last week’s hoopla surrounding President Obama’s first 100 days in office kept the spotlight trained on the health care IT portion of the stimulus package, but did little to tighten its focus.
To borrow a line delivered by the president in his 100th day speech, “I’m pleased with the progress we’ve made, but I’m not satisfied.”
There’s been a lot of buzz regarding the demand – and anticipated incentives – for broad implementation of electronic medical records (EMRs). While I salute this as an initial step toward improving patient safety and lowering healthcare costs, it’s not the giant leap forward that many believe it is. When projecting out what’s possible to accomplish with existing health care IT over the next decade, EMR implementation is more the starting block than the finish line.
The giant leap that this administration should be planning for is a more comprehensive and qualified use of EMRs. To be clear, HIMSS is playing an important role in defining meaningful use of EMRs, and its inclusion of clinical decision support CDS in that definition is a critical and telling move that simply digitizing and integrating patient data isn’t enough to achieve adequate improvements in U.S. health care. What’s missing is analytics.
As the CEO of an advanced CDS and clinical analytics company, I see every day the low-hanging fruit that payers can easily grasp with the development and implementation of an analytics strategy to accompany the implementation of EMRs.
Harnessing available data and translating it into truly actionable health intelligence is not an easy task, and by the same token, neither is creating an analytics strategy.
The universe of patient data that could be made available for analysis is mind-boggling, but even more stunning is the potency of intelligence that data represents. In addition to unwarranted privacy concerns, it’s the lack of analytics strategies that keeps even the most progressive health care companies from realizing the value of patient data.
Approaches to incorporating analytics into EMRs and other digital health delivery systems has been tentative and fragmented at best. Expectations are low.
Companies like Anvita Health have spent the time and energy to consider all the possible ways patient data can help payers, PHR and EMR vendors, and health care providers improve the quality of health care. A few examples:
· Analyzing health and pharma claims to generate intelligent inferences about a patient’s condition has the potential of both reducing false positives and uncovering previously undiscovered diagnoses.
· Using prospective analysis can predict for a hospital the probability of CMS “never events” happening to its existing patient base, and provide a precise prioritization of beds and related protocols to prevent these events from occurring.
· The use of real-time analytics can transform the corner pharmacist into a life-changing hero by giving him or her the tools needed for a personalized, behavior-driven intervention at the point of care, when patients are most likely to listen and learn.
I recently attended the World Health Care Congress where concepts like predictive modeling and real-time analytics are starting to get some traction in keynotes and hallway discussions. I’d like to remind my colleagues in the health care industry that these are not just concepts – they are analytics strategies that can be put to use now. All that’s missing is a more widespread understanding of how advanced health care analytics can be used to help organizations achieve what might otherwise be considered unreachable quality and cost savings objectives.
That understanding is the giant step we’re working toward, and it’s an essential step in realizing the meaningful change that this administration expects.
# # #
I’d like to welcome you to the Anvita Health Blog, where you’ll hear from members of the Anvita Health team on a variety of health care analytics issues.
Richard Noffsinger has served as CEO of Anvita Health since 2007. He has more than 20 years’ experience in the information technology and health care industries.