Adoption of electronic health records has been slow even with financial incentives. Clinicians are getting paid on quality instead of volume, and the quality measurements are ill-defined. Patients are initially engaged and motivated to adopt healthier behaviors via technologies like wellness apps, but they eventually lose steam and drop off completely. Life science executives need and want to make better decisions using simulations but fail to adopt this behavior change. What is going on?
Understand Behavior with Healthcare Simulations
The Complexity of Building Energy Consumption
It is estimated that buildings contribute 20-30% of energy use in the United States at an annual cost of over $100B. Buildings also contribute an estimated 35-40% of all US CO2 emissions resulting from building energy consumption. Any effort to decrease building energy consumption can thus have a substantial economic and environmental impact. Much of the effort invested in building energy efficiency and conservation is focused on analyzing or simulating individual physical systems within a building, to help designers understand, e.g., what savings could result by replacing standard lights with high-efficiency fluorescents, or by using light-colored paint on a building’s roof. Typical approaches combine simulating the actual physical properties of building systems, and statistical data based on historical usage. However, the complex interactions between building systems and the environment make accurate estimations difficult. The complexity of this problem increases dramatically when occupant behavior is included.
Simplifying the Complexity of Healthcare
With the passing of the US Healthcare Reform bill, we now have a more complex healthcare system than ever before, as shown in the detailed organization chart developed by the Joint Economic Committee minority. The chart displays a bewildering array of new government agencies, regulations and mandates
that appear to do little to simplify the complexity of powerful healthcare silos. These silos, while trying to please everyone, end up pleasing no one. Predicting how the interactions with this dizzying healthcare system impact our health outcomes is far too complex for the use of reductionist scientific approaches and traditional statistical analysis. What if we took a different approach? What if we modeled the patient’s perspective of how a person navigates the complex healthcare system in order to gain a view of their experiences and how this impacts their health outcomes? To understand how this concept works, let’s take the simple example of traffic. It’s very difficult to understand traffic unless you understand the behaviors of individual drivers. When you model the simple acceleration and deceleration of an individual driver, then can you start to understand some of the weird properties of traffic. For example, a traffic jam moves backward, but all the cars move forward. Applying this concept to healthcare, we can model the patient’s journey as they navigate the healthcare system seeking treatment for an ailment or choosing an insurance provider. What emerges are insights into what tipping points arise that help or impede a patient in their interactions with the healthcare system. Shouldn’t this be the measure of how to improve the healthcare system?
3 Ways Life Science Organizations Can Transform Themselves
What is the biggest obstacle for the life sciences industry? Yes, a productive R&D organization is up there. However, it is not the biggest hurdle. Negative public perception is the number one challenge. The “pharmaceutical company” label is negative and has hindered the value of both exceptional R&D people and innovative commercial people who are doing great work. The good news is that we are finally at an inflection point that will encourage a new commercial model while improving the negative perception that runs rampant. Negative perception aside, what can life science companies do to transform themselves? General Electric and IBM transforming from product companies to service companies are examples that we have all heard countless times. Three possible scenarios applied to life science companies include: 1. Wrapping a product around a pill For years we have heard how life science companies should focus on services around a therapeutic area and not just a treatment option. For example, offering behavioral interventions around diet and exercise for patients with type-2 diabetes as well as a treatment option could improve patient outcomes. 2. Transparent data liquidity Lack of data liquidity is hindering innovation among academics and life sciences. Sorting out who owns this data and how to make it accessible will unlock its power to be used for predictive analytics. Achieving a secure exchange of information across life science researchers and clinical care providers would increase efficiency and productivity. For example, once this exchange of information is available in oncology, predictive analytics can be applied to help researchers gain insights from larger patient populations with similar pathologies. 3. R&D collaborations New and innovative business models focused on IP should be created that empower anemic R&D teams. The current investment in drug discovery, development, approval and marketing is far too large. For example, a new model could extend collaborations from discovery through approval allowing experts who are most appropriate to a particular challenge to provide solutions. This approach would create a virtual team where IP is shared among people focused on a specific challenge. Could life science firms follow the trajectory of transforming themselves from product providers to service providers? Simluations of these new commercial models using software is a low-risk way to explore the benefits of these new ideas for life science companies and for the public.
9 Ways to Apply Predictive Analytics to Healthcare
1. Model drug development collaborations that maximize IP and drug discovery.
2. Simulate PRO (Patient Reported Outcomes) for care quality improvement and outcomes.
3. Accelerate time to market for new therapies with strategic portfolio modeling.
4. Predict market access and optimize resource allocation for new therapies.
5. Predict high risk patients for ACO (accountable care organization) and hospitals.
6. Leverage advanced analytics to reduce hospital readmissions.
3 Reasons the Nantucket Effect Impacts the Complex U.S. Healthcare System
At the end of the day you do what you get measured on. Nestled in one of the world’s most beautiful islands, rural Nantucket Cottage Hospital has become yet another example of how simple decisions by powerful people can negatively impact the complex healthcare system. What is going on? Under a complex Medicare hospital payment system, Nantucket Cottage’s rural designation has allowed the state of Massachusetts’ 81 other hospitals to collect an estimated $367 million annual bonus.
Freedom to Occupy
On the evening of May 1, 2012, as I enjoyed a twighlight jaunt along Battery Park in New York, I heard what sounded like parade in the distance. Curious about its source, I walked toward it and realized it was a May Day protest march organized by the Occupy movement. The march, which had begun a few hours earlier in the vicinity of Union Square, snarling traffic throughout midtown and lower Manhattan, had reached the Financial District and was being carefully monitored and corralled by a large police force that had erected barriers all along the route in an attempt to impose some order upon the chanting, sign-wielding, drum-beating masses.
Boston Public Transportation Blues
Having lived in various parts of the Boston Metro area for 24 years, I have experienced the local public transportation system from many angles: at different times I have been a regular user of the bus, the “T” (Boston’s nickname for its subway), and the commuter rail – all managed by the Massachusetts Bay Transportation Authority (MBTA). I have also experienced public transportation in many cities around the world: I carry active public transportation cards for Boston, New York, Washington, Chicago, London and Rome. Given the choice, I would much rather take public transportation than a taxi or car. Sadly, I have found that in some ways, Boston’s public transportation system is uniquely and frustratingly inadequate.
Dealing with Budget Cuts in the Military: Reframing Budgetary Decision-Making
After a decade of fast-growing budgets, military decisions makers are now facing significant budget cuts and must allocate resources accordingly. A number of military officers and senior civilians are confronted with a down budget for the first time as decision makers. Their mental framework for allocating resources and prioritizing needs is entirely defined by their experience of a growing budget and dramatically fewer constraints than today. As a result, the prevailing decision heuristics (the subconscious cognitive mechanisms, or “mental accounting”, by which humans make decisions) in use today are heuristics that worked in a very different environment and under very different constraints, and are unlikely to perform adequately in the new budget environment particularly during a transition period.
Cybersecurity: Behavior, Behavior, Behavior
Every day we hear reports of new cyber-threats, and every single time they point to the same culprit: people as the weakest link in cyber-security. In addition to my earlier rant on cybersecurity and human behavior, a great piece was posted a few weeks ago in Government Computer News that articulates the issue very well. A case in point is the recent drone virus revealed by Wired. It is a great example of the lack of appreciation for the tradeoffs you need to make when running missions. After the 2008 incident in which an infected removable media drive was the vector of entry for a worm into an overseas secret-level DoD network, the use of USB drives has been severely restricted throughout the military.