Healthcare Reform Debate Continues

Now the healthcare reform political debate has switched to two topics about whether states should implement: (1) Health Insurance Exchanges (HIE); and (2) Adopt the new formula that determines who is eligible for Medicaid.  The new formula would provide greater coverage for lower income individuals and families.

There should be zero political debate about whether HIE's are good for taxpayers and consumers-- they are.  End of debate.  Insurance companies don't want them because they will eventually drive up competition and drive down premiums.  Many GOP governors don't support HIE's simply because they prefer to oppose anything to do with Obama.

The #2 issue is more complicated and worthy of healthy debate, not along political party lines.  While expanding Medicaid coverage is a good thing to do socially-- providing insurance for those who can't afford to pay seems like the humane thing to do in our country.  The problem is, many states can't afford to fund expanded coverage right now, nor can the federal government.  So... it's the right thing to do socially, but a foolish thing to do financially at this time in history, without a plan to afford the increase in coverage.

Remember: I'm a registered political Independent and have been my whole voting life.

Suggestions About Predictive Analytics

Suggestive Analytics. The power of suggestion meets the power of data. But, before we talk about that, let’s contrast it to the buzz phrase of the day — Predictive Analytics.

I’d like to have a dime for every white paper, blog, journal article and marketing brochure I’ve seen in the past six months that cheer a coming healthcare revolution as a result of “Predictive Analytics.” The only thing more prevalent is the term “Big Data.” The latter fascination and buzz is rooted in Freudian reference, I’m quite sure, so of course every male CIO in healthcare wants it.  J But what of the former — what about predictive analytics?  On what do we base this sudden love affair with predictive analytics?  Not much, I’d say.

Wikipedia has a fairly lengthy definition of “Predictive Analytics.”  As defined there, “Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events.”

Seems to me that predictive analytics in healthcare tends to come in two flavors — too easy and too hard — with a neglected middle ground.  Do we really need predictive analytics and Big Data to know that a 32-year old sedentary patient who smokes and has a BMI of 30 is a high risk for multiple chronic diseases?  What about a patient living alone, over 65, post-CABG — do we need Watson to tell us that such a patient is high risk for readmission?  Hardly… and yet we are surrounded by such patients.  The problem isn’t that we don’t know.  The problem is that we don’t intervene.

At the other extreme are the patient outliers and rare cases.  As much as we would hope and try, no computer algorithm in the near future is going to predict the impending stroke of a 34-year old patient who is a competitive triathlete with no family history of cardiovascular disease, yet we pursue such predictive scenarios and celebrate enormously if we come within a country mile in a randomized trial of pulling it off.  We ignore the forest of high-risk patients that surrounds us, in pursuit of the perfect and isolated tree.

The predictive middle ground of data that we choose to ignore at the point of care is genetic and family history.  Depending on which report you believe, we know of at least 150 genetic and family history markers that have a profound impact on the outcome of care. Think about it — we can predict and manage care with genuine data-driven decisions in these cases, but we ignore this middle ground, in part, because the data is not readily available in EMRs at the point of care (a different problem altogether worth discussing), we ignore these opportunities for the same reason we ignore the forest of “too easy” scenarios.

Predicting clinical outcomes and risk is not the problem.  The problem lies in our inability and unwillingness to intervene — personally, culturally, and operationally — when we see the opportunity. What healthcare system is operationally capable or even culturally willing to assign a caregiver at home to ensure that the 65-year-old post-CABG patient will not be readmitted?  How many women are willing to be tested for BRCA1 and BRCA2 mutations that raise the risk of breast cancer by 60% … and take action if they are affected?  I won’t even talk about our inability to do something about the skyrocketing incidence of obesity and diabetes.  It’s not that we don’t know, it’s that we are generally incapable of intervening.

Does this cynicism mean I have no interest or hope in predictive analytics for healthcare?  No.  We need to continue inching along technically and culturally with the concepts until, someday, the two will intersect.  In the meantime, we should be realistic and look for other analytic opportunities that are within the grasp of healthcare and already surround us in other parts of our lives — which brings me back to this notion of Suggestive Analytics.

I first saw the power of suggestive analytics in a program called the Antibiotic Assistant at LDS Hospital, thanks to colleagues Dave Classen and Scott Evans.  The Assistant is a complex algorithm which predicts and ranks the best course of antibiotic therapy for inpatients, given the profile of their lab, micro and pathology results, and general demographics.  It’s a very impressive, and an early example of predictive analytics.  However, to me, the equally impressive story is its use of suggestive analytics.

When the program was first released, only the predictive efficacy of the rank ordered antibiotic protocols were presented to physicians.  Naturally, physicians always chose the highest ranked protocol, even if the predicted efficacy of the top choice only differed by a tenth of a percentage compared to the second best choice, but that second best choice might be 10 times less expensive than the top choice.  The breakthrough came when Scott and David revealed the cost of those antibiotic protocols to the physicians, thus suggesting to physicians that they also consider cost alongside predicted efficacy when making their decision.  The benefits to clinical outcomes stayed virtually the same, but costs dropped from an average of $123 per dose to $52.

As e-commerce consumers, we are surrounded by Suggestive Analytics.  Amazon was among the first to influence our behavior by using data in this capacity.  They surround our transaction — e.g., buying a book — with data-driven suggestions that affect our purchasing behavior — customer ratings, frequency of purchase by other consumers, commonly bundled and related purchases and products, availability of the product, arrival date of the shipment, new vs. used prices.

Richard Thaler and Cass Sunstein wrote a great book (Nudge:  Improving Decisions About Health, Wealth, and Happiness) that gives example after fascinating example of what amounts to suggestive analytics, even though they never specifically use the term.

The difference between predictive and suggestive analytics is summarized quite easily:  under predictive analytics, Amazon would fill your shopping cart for you, based upon using predictive data mining algorithms.  Under suggestive analytics, you get to fill your own shopping cart.  Would I prefer that Amazon predict my shopping habits for me and streamline the whole process without my intervention?  Absolutely.  Is it reasonably possible in the near future?  Absolutely not.

Predictive analytics is certainly appealing in concept but, right now, it is little more than a marketing term, another adjective of hype.  Our healthcare industry would be better served to borrow concepts from the world of e-commerce and social networking, and embrace a new concept of Suggestive Analytics at the point of care to nudge our behavior in desired directions.

Personal Honesty: Is Facebook the Real You?

Social media, especially Facebook, is among other things, a life experiment for me as an IT professional and "corporate executive". In particular, I'm insatiably curious about the role that social media technology plays in transparency-- breaking down the barriers and often hypocritical behaviors between personal and professional lives that allow for things such as university football coaches to lead double lives as pedophiles; Secret Service agents with the trust of Top Secret clearances to cheat on their spouses with prostitutes; and Board members and executives to conspire and misappropriate money from non-profit healthcare organizations-- i.e., how truthful is the complete circle of our lives? The recent flap over employers asking for Facebook passwords is an interesting twist on personal honesty vs. personal privacy-- should we care if we have no secrets? Does social media reveal the truth and real person in us or not? This study from the University of Texas seems to think so...  Manifestations of Personality in On-Line Social Networks.

None of us hold perfect behavior.  Accepting that, if you are going to make a mistake in personal behavior and organizational leadership, err towards transparency, not secrecy.  Open up your life.  If the worst thing they accuse you of is "too much honesty... too much information", I suspect you'll still get a clear pass through the Pearly Gates.  :-)

Healthcare Change is Here to Stay, Regardless of Supreme Court?

"Do you agree that Change is Here Regardless of Supreme Court's Decision?"

This question was recently posed by Jay Warden of the Camden Group to members of HIMSS.  Copied below was my response.

I believe SCOTUS is going to overturn the individual mandate, but leave the rest of ACA alone for Congress to fix. But, to the question, yes, I believe we finally reached a tipping point in the industry towards what I'm calling Healthcare 2.0, which boils down to bundled payments that put the economic burden of quality and cost control on the provider, neither of which exist under the current fee-for-service model. In the absence of federal pressure to change healthcare if the Affordable Care Act (ACA) is repealed-- partially or completely-- the employers now have the momentum and there's no turning back.

We are seeing a growing trend of direct contracting with providers by self-insured employers, which is disintermediating the insurance companies and their profits from the economic model. The federal individual mandate under ACA was foolish and naive-- and pushed by the insurance companies to line their pockets. We simply aren't the type of society that legislates that sort of thing from the federal level, and trying to do so was like poking a stick in a hornet's nest. But, the ACA is not all bad and I will give the ACA credit for getting the ball rolling. Employers like IBM, Cisco, Intel, Google, Facebook, Apple, WalMart, Ford, et al, are not going to allow the momentum stop. They are taking control of healthcare by squeezing traditional insurance companies like Aetna, BCBS, and United out of the picture and direct contracting for fixed-fee coverage-- or in some cases, hiring and building their own healthcare delivery organizations.

I'm a CIO in a national health system that allows for private and federally insured options and thus provides 98% insurance coverage without an individual mandate. In this model, if you want insurance for broader coverage, you can buy that in the private market. If you want very affordable, basic coverage, you can access that through the federal government or similar products that are legislated upon the private insurance companies. It strikes a very good balance between private market and federal involvement to ensure access and affordability. I'm also a part-owner of a small business that is leveraging new models for self-funded coverage; it's not easy but it is possible. Obama and Congress could have made it easier for all employers by repealing the McCarran-Ferguson Act (MFA) and opening the doors to interstate competition between insurance companies. Legislating insurance that's not affordable is like creating 30 million new criminals. You need to lower the costs of insurance, first, and you can do that by removing the federal laws that protect the insurance companies from open competition. The Obama Administration caved to the insurance lobby by allowing MFA to stand...since the 1940s.

Despite a less-than-perfect federal attempt, the ACA got things rolling and the momentum for healthcare change is hear to stay, thanks to employers who are no longer willing to accept the status quo.

EMR User Interfaces: Embedding Team-Based Care and Knowledge

There's a great new article in the March 2012 issue of The American Journal for Managed Care, "IT-Enabled Systems Engineering Approach to Monitoring and Reducing ADEs".  You can find it here.  The point I'm trying to make in this blog, by drawing attention to this article, is this:   It's time for EMR vendors to rethink the fundamental design and user interface of of their products.  They've been basically unchanged for 10 years, at least, supporting the same concept of encounter-based care that itself goes back at least 50 years.  EMR vendors need to stop thinking of patient care as short term encounters with a clinician and start thinking of patient care as long term project management with a team of care providers.  EMRs user interfaces need to look more like a project management tool that enables social collaboration, task assignment and follow-up, and the collective wisdom of the care team, including the patient's wisdom, more effectively.

In summary, the AJMC article concludes that, by using web-enable team resource management (TRM) tools, clinical work teams can significantly reduce the rate of adverse drug events in patient care.  According to the article, "the rate of ADEs decreased from 25.8 to 18.3 per 100 patients per year in the intervention group. The rate was virtually unchanged in the control group (24.3 vs 24.8)."  That's a major impact.

The study described in the article is particularly impressive in that, the TRM tool leveraged the collective wisdom of the care team in identifying, tracking, and measuring the causes and interventions associated with ADEs.  It's a prospective model for preventing ADEs versus a retrospective root cause and failure modes analysis of what caused the ADE.  Very, very impressive approach.  The team at  the Department of Family Medicine and UB School of Management, State University of New York, should be widely acknowledged for this innovative yet common-sense approach to patient care.

EMR vendors, please take note.  :-)

Healthcare Reform Is Being Debated in the Supreme Court Today

The debate focuses on whether the "Individual Mandate" for healthcare insurance is constitutional or not. This is a critically important day for the US...this debate has implications about constitutionality and federal authority that extend far beyond just healthcare. If I were President of the US, trying to improve healthcare quality and control healthcare expenses for everyone, I would advocate in favor of the Individual Mandate. If I were a judge and constitutional lawyer, I would advocate that Congress had overstepped the bounds of the Constitution-- i.e., Individual Mandate should be overturned at the federal level, and deferred to each state, much like the "other" individual mandate for automobile insurance that is now required in virtually every state. But if Individual Mandate is overturned at the federal level, the state governors better get their act together, quickly, otherwise all of us will suffer under the continuance of a dysfunctional healthcare economic system.

Cayman Collaborative Care Initiative

26 March 2012

To:  Interested Vendors

Dear Colleagues,

The national health system of the Cayman Islands, also known as the Health Services Authority (HSA), is developing a physician compensation model that includes physician productivity and clinical quality as risk variables to total compensation.  The project is known as the Collaborative Care Initiative (CCI). To these ends, the HSA is conducting an options assessment for vendors and products that can meet the reporting requirements to support the CCI metrics for productivity and clinical quality.   HSA defines the overall value of healthcare as follows.   Vendors must be capable of supporting this conceptual definition with their solution.

  

That is, the overall value of healthcare is greatest when the quality of care is delivered in a cost-effective manner.  Cost and quality are inextricably linked.  In this early iteration of CCI, physician productivity is being used as an inverse proxy for cost of care—highly productive clinicians reduce the overall cost per unit of high quality care produced.

Below are the high level requirements for CCI metrics and reporting.  HSA encourages vendors to suggest additional requirements and functionality to ensure the best overall solution.

1.     Timelines:  HSA will schedule vendor meetings and product demonstrations immediately, on a first-come/first-served basis.  The initial operational date for CCI reporting is July 1, 2012.

2.     Contact Information:  Interested vendors should contact Mr. Keith Higgins, Acting CIO, at keith.higgins@hsa.ky, to arrange demonstrations and discussions of their solutions.

3.     Cerner-Centric:  The HSA currently operates a full complement of Cerner Millennium products, operating in Cerner’s Remotely Hosted Option (RHO) in Kansas City.  The vendor’s product must be capable of extracting data from Cerner for purposes of reporting and analysis. 

4.     HSA Hosted Data:  The data extracts from Cerner must reside within the administrative and operational environments of HSA. Therefore, the data must be hosted in the Cayman Islands—either in HSA’s data center or a data center that is currently associated with HSA administration—or in Cerner’s RHO data center in Kansas City.

5.     Adaptable Data Extracts, Model and Content:  At present, Cerner is the primary source of data for clinical quality and physician productivity measures, however, HSA may, in the future, divest itself of the Cerner system.  Therefore, the vendor’s solution must be capable of functioning with very little modification, and minimal additional cost and time if this divestiture occurs.  HSA also utilizes a large number of standalone spreadsheets for tracking various aspects of the care process.  These spreadsheets could be useful in the context of CCI. The vendor’s solution must be capable of including this spreadsheet content by either extracting and integrating the data, or by accommodating the data collection directly in the vendor’s solution.

6.     Reporting Requirements:  The vendor’s solution must support the following reporting environments:

a.     JCAHO ORYX

b.     HEDIS

c.     PQRS

d.     Meaningful Use

e.     Pioneer Accountable Care Organization reporting

f.      Patient reported satisfaction with care

7.     Physician Productivity:  HSA believes that there are no perfect models for measuring physician productivity and therefore, compromises in this regard will be necessary.  Vendors are encouraged to suggest new and innovative models.  Traditional measures of physician productivity, especially revenue-based, are not appropriate in HSA’s context.  HSA is a national health system, therefore revenue generated by HSA is realized as an expense to the Ministry of Health and the Cayman Islands National Insurance Company (CINICO).  This tension between revenue vs. expense is the same in US-based direct contract agreements between self-insured employers and healthcare systems—a growing market trend in the US.  RVU-based models are not appropriate within HSA’s environment because the RVU model is CPT-driven, thus rewarding volume of clinical procedures over quality of clinical outcomes.  Furthermore, HSA and CINICO are migrating away from a fee-for-service/CPT-based reimbursement model towards a capitated, fee-for-quality/ICD-based reimbursement model.  At present, HSA believes that the best overall measure of physician productivity is based upon patient access to care, as measured by clinical encounters per unit of physician time worked, including telephone and email encounters.  Future models of productivity may emerge, therefore the vendor’s software solution must be adaptable and agile.

8.     Inpatient and Outpatient Duties:  The measures of quality and productivity must accommodate the physicians’ inpatient and outpatient duties.

9.     Administrative Duties:  The measures of productivity must accommodate the administrative duties associated with physicians’ roles, such as staff meetings and especially those meetings that are associated with care planning and clinical quality initiatives that impact broad issues of patient care, but are not specific to an individual patient.

10.  DRGs:  HSA does not currently utilize DRGs, therefore the vendor’s solution cannot be dependent on DRGs.

11.  References:  The vendor must provide at least three referenced accounts who are using the vendor’s solution in initiatives which are very similar to CCI.

12.  Affordability:   HSA is equivalent in size and scope of services to a rural health system in the US, with similarly modest access to operational and capital funds. The Total Cost of Ownership (hardware, software, labor services) for the vendor’s solution will be evaluated early in this options assessment.  Vendors will be expected to engage in these discussions accordingly and will be quickly eliminated from the procurement, regardless of rich functionality, if their solutions are not affordable within the context of rural healthcare.  If and when a contract is awarded, the contract will be firm-fixed price, with modest annual support fees.  HSA’s total operating budget is $85M per year.  The total bed count is 125.  HSA employs approximately 750 people, of which 40 are physicians.

Thank you and please contact Mr. Higgins if you have any questions or interest in this procurement.

Warm regards,

___________________________

Dale Sanders

Senior Technology Advisor/CIO Mentor

dale.sanders@hsa.ky