Localize to Democratize Heathcare

The role of governments in healthcare, including information technologies, is such a politicized issue, it’s hard to find objective and in-depth analysis of options, benefits, and costs. Virtually every voice is an apologist or protagonist of one position or another interfering in the type of objective analysis that is necessary to have a factually-based, fair, and efficacious solutions.

For example, a 2014 study by the independent Pew Research Center found that government involvement in healthcare regulation is heavily supported by liberals, 89% of whom opined that it is the role of the federal government to ensure health insurance of citizens; however, the general public feels differently with only 47% feeling the government has some responsibility and 50% opining that it is not a responsibility of the federal government. (Pew Research Center, 2014)

Two often overlooked elements in the debate about the most appropriate and ethical role of government in healthcare regulation, including things like genomic uses and the creating and promulgation of standards for technologies, are geography and symmetry of information. Asymmetry of information between patients, patients and providers, technology and technology vendors can create monopolistic type conditions. Intentional applications of asymmetric understandings or information can also result in unethical conditions, for example, pharmaceutical companies repetitively exposing patients to advertisements creating an unequal impression compared to providers’ recommendations. 

Geographically, the needs for government intervention differ because the needs of the people and companies in those regions differ. For example, it would make no sense to regulate the use standards of genomic data or block chain security in India, a country where hundreds of millions of patients still see untrained physicians for a $1. In some countries, these needs differ by region – wealthier regions of India or wealthier US states have different ethical needs for government healthcare regulation than poor regions or states. Alaska’s problems and needs are different than New York.

Government often involves itself in ways that, however well-intended, actually cause harm or massive monetary wastes or opportunity costs – the cost of not doing higher priorities. To quote global news analysis periodical The Economist: “Two forces make American laws too complex. One is hubris. Many lawmakers seem to believe that they can lay down rules to govern every eventuality… The other force that makes American laws complex is lobbying. The government's drive to micromanage so many activities creates a huge incentive for interest groups to push for special favours.” (The Economist, 2012) 

Therefore, this author would advocate these two decision criteria be applied to the discussion of how, when, why, and how government involves itself in healthcare regulation. One way this could be performed objectively and without political or special-interest influence would be for independent, fully-funded, expert panels without ties to any industry or political agenda to prioritize the information needs by audience by region. Thereafter, funding from the federal governments could be provided to state and regional governments to locally administer and address their unique informational needs. 

This model can also be applied to technologies and medications, and at a macro or global level, possibly through the United Nations or World Health Organization. A global panel would, for example, be most appropriate for information technology standards because most technology innovators and pharmaceutical manufacturers and insurance companies are multinational and work around, in the most efficient market-driven manner, inconvenient regulations proposed by any one country. The advent of cloud-based software-as-a-service (SaaS) aligns with these work arounds closely; the model proposed would make such work arounds more difficult to execute.

One challenge though with this approach is distrust between governments. As of spring 2015, 67 countries or regions were attempting to enact data residency requirements for their citizens’ data. In Argentina, Canada, Israel, Russian, and parts of the European Union, financial and health information on their citizens may not leave the country making Big Data analysis and global health approaches much more difficult, even with the option of tokenized or anonymized data. (Hawthorn, 2016)

Works Cited

Hawthorn, N. (2016, October 29). 74% of Cloud Services do not Meet European Data Residency Requirements. Retrieved from Sky High Networks: https://www.skyhighnetworks.com/cloud-security-blog/74-of-cloud-services-do-not-meet-european-data-residency-requirements/

Mor, N. (2015, December 21). What role should governments play in healthcare?Retrieved from World Economic Forum: https://www.weforum.org/agenda/2015/12/what-role-should-governments-play-in-healthcare/

Pew Research Center. (2014, June 12). Political Polarization in the American Public.Retrieved from Pew Research Center: http://pnhp.org/blog/2014/06/13/pew-research-on-the-governments-role-in-health-care/

The Economist. (2012, February 18). Over-regulated America: The home of laissez-faire is being suffocated by excessive and badly written regulation. The Economist, p. http://www.economist.com/node/21547789.

Changing Cancer Care

According to the World Health Organization (WHO), cancer is now the leading cause of death worldwide, with approximately 14 million new cases and 8.2 million fatalities annually, which is predicted to grow by 70% over the next 20 years. (World Health Organization , 2015) Four problems profoundly impact cancer clinical outcomes in the world today: (1) non-adherence, because of increasing use of oral cancer agents; (2) coordinating care, because cancer care is team-based across disparate providers (poly-pharmacy & poly-provider); (3) drug-drug interactions, because at least 2.24 million cancer patients annually experience serious interactions, and 89,600 die as a result (van Leeuwen RW, 2013) and, (4) patient education, because cancer care is complex, and targeting and delivering information largely impacts clinical outcomes.

Oral cancer therapies are increasingly the treatment of choice for cancer, and require longer treatment plans.  Medication non-adherence rates for oral cancer agents range from 21% (2.94 million) to 28% (3.91 million). Moreover, 69% of cancer patients have been found to rely on caregivers whose estimate adherence at 99%, but are incorrect 50% of the time. (Stefan Feiten, 2016)

Because American adults now see and average of 19 providers in their lives (28 for those over age 65) coordinating care across disparate health systems or pharmacies is increasingly necessary and difficult. (Practice Fusion, 2016)  The American Society for Clinical Oncology has noted the “…increasing emphasis on use of the medical home model for delivery of care is driving greater emphasis on team-based care by health providers from a variety of backgrounds and specialties, including primary care physicians, urologists, gynecologists, pathologists, pharmacists, genetic counselors, mental health specialists, pain and palliative care specialists, and advanced practice providers.” (American Society of Clinical Oncology, 2016)  However, as noted by the Institute of Medicine, the “fragmented cancer care system…impedes coordinated care and the development of comprehensive treatment plans.” (Institute of Medicine, 2011) Moreover, because cancer is a collection of diseases, and not one disease, customizable treatment plans are critical.

Personalized targeting and delivery of information is also critical in cancer care.  Studies have found 96% of patients specifically wanted to know if they had a type of cancer, and 79-96% of patients wanted as much information as possible; however, a plethora of studies indicate serious deficits by providers in accurately assessing and delivering information about their care to patients. (Aoife Drew, 2002) The intervention strategies recommended by treatment leaders, including:

(1) Practice guidelines for oral cancer agents via patient monitoring and feedback in real-time, a multi-component intervention (e.g., text reminder devices, targeted provider feedback), education, and depression by identifying it via assessment surveys and referring it to healthcare teams for treatment; (Spoelstra, 2015)

(2) Coordinates care, optimizes, and reconciles medication regimens; and, (Cutler, 2010)

(3) Insures patients neither miss doses, nor incorrectly take them. (Walker, 2015)

Works Cited

American Society of Clinical Oncology. (2016). The State of Cancer Care in America: 2016. Alexandria: American Society of Clinical Oncology.

Aoife Drew, T. F. (2002, April 1). Responding to the information needs of patients with cancer. Nursing Times, pp. https://www.nursingtimes.net/responding-to-the-information-needs-of-patients-with-cancer/199393.article.

Cutler, D. (2010). Where are the health care entrepreneurs? The failure of organizational innovation in health care. Innovation Policy and the Economy, https://dash.harvard.edu/handle/1/5345877.

Institute of Medicine. (2011). Patient-Centered Cancer Treatment Planning: Improving the Quality of Oncology Care. Washington DC: National Academies Press.

Practice Fusion. (2016, September 12). Survey: Patients See 18.7 Different Doctors on Average. Retrieved from PRNewswire: http://www.prnewswire.com/news-releases/survey-patients-see-187-different-doctors-on-average-92171874.html

Spoelstra, S. (2015). Putting Evidence Into Practice: Evidence-Based. Clinical Journal of Oncology Nursing, 60-72.

Stefan Feiten, R. W. (2016). Adherence assessment of patients with metastatic solid tumors who are treated in an oncology group practice. Springerplus, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4777967/.

van Leeuwen RW, B. D. (2013). Prevalence of potential drug-drug interactions in cancer patients treated with oral anticancer drugs. Br J Cancer, http://www.ncbi.nlm.nih.gov/pubmed/23412102.

Walker, J. (2015, December 31). Patients Struggle With High Drug Prices. The Wall Street Journal, pp. http://www.wsj.com/articles/patients-struggle-with-high-drug-prices-1451557981.

World Health Organization . (2015). Cancer Facts & Figures 2015. Geneva: World Health Organization.

The Mysteries of Predicative Analytics

The two things that are most surprising about the burgeoning field of predicative analytics are: (1) the approaches people are taking to it and their varied efficacy; and, (2) the degree to which people appear to recognize its conceptual value; however, appear unsure how to use it or proceed.

Predicative analytics is the promise to do nothing less than predict the future.  And, the source of motivation in corporate executive suites appears to be largely based on envy.  In other words, executive read about technology juggernauts like Google or Facebook exploiting data and want what appears to be magical insights too for their own benefits.  This digital version of “keeping up with the Jones’” appears to be accelerated, like fuel on a fire, by management consultants from prestigious institutions, like Andrew McAfee and Erik Brynjolfsson of MIT reporting corollaries of 5-6% higher profits for those companies that have any “Big Data” and advanced analytics initiative.  (Dominic Barton, 2012)

Yet most companies, according to industry experts, remain either lacking in confidence, unsure how to move forward, or both.  While some of these experts opine the source of delay is buyers’ remorse from historically expensive and underperforming “latest and greatest” technologies like enterprise resource planning (ERP), data warehousing, or customer relationship management (CRM), my impression is that the answer is found in a 13^th century English Franciscan monk known today as Occam, or more specifically Occam’s Razor.  Simply put, it states that the simplest explanation has the highest probability of being correct.  In this case, people understand the idea of predicative analytics conceptually, but know neither the details of what it entails nor the understanding as to how to employ it.

Predicative analytics is an inter-disciplinary field combining mathematics, computer science, and subject-matter expertise.  The good news is that means it attracts scholars and researchers from three different fields; the bad news is that unless someone has knowledge in all three, they can only design things they can’t build, build things they can’t design, not design and build things that are useless or don’t provide a solution because they lacked the subject-matter expertise to know the problems or right questions to ask.

The second surprise can be seen by how hard it is, even at distinguished and premier universities, to find a concise summary of what predicative analytics entails.  As such, one can learn more from an hour on Wikipedia than conceptual lectures about the future potential of the field.  For example, one could say there are three major models, x number of software providers and what each does, and 18 major algorithms based on statistics, inference, linear regression and machine learning. (Finlay, 2014)

The second surprising problem feeds the first.  That is, because of a scarcity of primers explaining predicative analytics in a way that learners know about architectural, product, and algorithm choices, they are unable to easily expand on their source field knowledge to the other elements of this interdisciplinary field.  Finally, because there is such potential value in employing the knowledge competitively, there is little motive to share and make this specialized knowledge available, and instead, great motives to create an intellectual oligopoly to prevent competitors from prevailing.

Works Cited

Dominic Barton, D. C. (2012, October). Making Advanced Analytics Work for You. Harvard Business Review, pp. https://hbr.org/2012/10/making-advanced-analytics-work-for-you. Retrieved from Slideshare.com: http://www.slideshare.net/mitchki/predictive-analytics-context-and-use-cases

Finlay, S. (2014). Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods. Palgrave Macmillan.

Health Care Price Transparency Meets Antitrust

Consumers are increasingly seeking health care price transparency largely to enable them to shop for health care services, (Kutscher, 2015) and because of a new patient interest in value, stemming from patients paying a larger portion for their health care because of increased deductibles and co-pays. (Betbeze, 2016)  Also, alternative care organizations (ACOs) and similar arrangements wherein the provider accepts more risk for the cost and outcome of medical treatment has providers desiring cost and price transparency to demonstrate their market competitiveness.  (Betbeze, 2016)

One leader in the movement is Blue Cross & Blue Shield of North Carolina who, in 2015, decided to publish the prices it pays to certain facilities for specific procedures and health care services.  Some providers responded negatively to the disclosures because they perceived it made them appear like they were getting a larger amount of money than some patients felt comfortable with for their services. (Betbeze, 2016) One result of the movement is a commoditization of services that are discrete procedures, making them more transactional and market competition pushing the prices for the lower.  Some experts estimate as much as 40% of all health care services will thusly be commoditized in the United States.  If these market forces continue on their current trend, it may make it difficult for many providers to invest in their practices because higher-margin procedures will be more difficult to secure because patients know they are high-margin and will shop around, limiting practice investments to affluent founders or investors. (Betbeze, 2016) 

One of the primary concerns from professionals familiar with cost information from major payers is the denominator of value, which is quality.  They state that while quality standards exist, they are today not married with pricing or cost data making equal comparisons difficult at best. (Kutscher, 2015)  Moreover, patients have actually complained after initial disclosures were made that they sought because the data is difficult for them to interpret such that it appears irrelevant. (Betbeze, 2016)

Another peripheral movement toward health care cost transparency comes from efforts primarily designed to improve health care quality and value. Twenty-two (22) states (See Figure 1) have implemented or are implementing a medical home model, or patient-centered medical home (PCMH), initiative aimed to improve quality and lower costs. (Patient-Centered Primary Care Collaborative, 2016) (AcademyHealth, 2010) To ensure consistent pricing among different insurance companies (payers), insurance companies that typically compete are required to coordinate price lists and/or payment policies, an activity that would typically be prevented by antitrust rules and decrease competition. (AcademyHealth, 2010)

Works Cited

AcademyHealth. (2010). Navigating Antitrust Concerns in Multi-Payer Initiatives. Retrieved from AcademyHealth.Org: https://www.academyhealth.org/files/publications/AntitrustMultipayer.pdf

Betbeze, P. (2016, January 4). Big Ideas: Healthcare Price Transparency: Patients and Payers Versus Providers? Health Leaders Media, pp. http://www.healthleadersmedia.com/leadership/big-ideas-healthcare-price-transparency-patients-and-payers-versus-providers.

Kutscher, B. (2015, June 23). Consumers demand price transparency, but at what cost? Modern Healthcare, p. http://www.modernhealthcare.com/article/20150623/NEWS/150629957.

Patient-Centered Primary Care Collaborative. (2016, May 22). Primary Care Innovations and PCMH Map by State. Retrieved from Patient-Centered Primary Care Collaborative: https://www.pcpcc.org/initiatives/state

Telehealth: The Bleeding Edge of Medical Technology

Telehealth, like all things, has strengths and weaknesses, has had successes and failures, and like most health IT (HIT), has experienced varying degrees of adoption challenges.  According to the most often cited study published in peer-reviewed journals (289 times), in a cross-specialty sample of 3250 patients (1625 subjects and 1625 controls) over 12 months, telehealth interventions correlated to an 18% reduction in hospital admissions, 14% reduction in emergency room visits, a 3.7% reduction in mortality, and an almost one day (4.87 versus 5.68 days) shorter hospital stay. (Adam Steventon, 2012) 

In the second most oft-cited study published in a peer-reviewed journal, telehealth was analyzed for its impact on diabetes care over a one-year trial with randomly selected subjects.  It found that telehealth interventions correlated to a 12.8% reduction in glycated hemoglobin (GHb) from baseline measurements in the first six months (traditional care showed a reduction of 2.27%). (Richard M. Davis, 2010)

Like other aspects of health IT though, a primary telehealth challenge is in its adoption rate.  In a 2011 survey of 1300 nurses by the Royal College of Nursing, 20% of respondents perceived electronic medical records as a “threat to the nursing-patient relationship.”  Over 50% had never heard of telehealth, and 82% of those who had heard of it perceived it would have no impact to nursing care. (Cook, 2012) 

A National Health Service (UK) Confederation report identified deeper issues within the medical community as to the impacts that telehealth, and other forms of HIT, have on the sociology and psychiatry of medical treatment that slows adoption.  Namely, it identified three core issues affecting adoption rates: (1) power and identity Issues from patients being more informed and having access to providers to ask many questions changing the traditional provider-patient relationship; (2) trust issues from either the provider or patient not trusting the technology or low confidence in their use of it; and, (3) equity issues if access to the technology, for whatever reason(s), differs by provider or patient community, which can actually worsen accessibility to health care in some cases. (Cook, 2012)

Another perceived weakness can stem from the simple efficacy, or lack of efficacy, of the technology; in other words, does its application and use improve the intended clinical outcomes or not.  In an article published in The Wall Street Journal just three days ago, it summarized a study being published in JAMA Dermatology wherein researchers posed as patients with skin problems had sought diagnoses from 16 providers using telehealth (seven general medicine and nine specializing in dermatology).  The researches encountered problems with physicians never reviewing the patients’ medical histories, failing to disclose possible adverse events of treatment (84% of cases), and in two cases, were diagnosed by foreign physicians who failed to meet the required local licensing requirements where the patients resided.  Moreover, they misdiagnosed second-stage syphilis (88% missed), an aggressive form of herpes spread through eczema (78% missed), and an aggressive form of skin cancer (21% missed), the consequences of delayed diagnoses for two of which, could prove fatal.  One-hundred percent (0 of 12) recognized polycystic ovarian syndrome (POS). The results were characterized by an independent physician from Harvard Medical School as identifying “egregious” examples of quality-of-care issues in telehealth.  It also noted that an industry group attempting to certify telehealth companies for quality assurance has had 500 applicants; however, only seven (7) companies have been approved. (Beck, 2016)

Closer scrutiny of the studies showing the strengths or benefits of telephone also demonstrate an apparent bias to show more positive clinical outcomes than actually exist. For example, in the most-oft cited study noted above herein, it failed to establish a causal relationship between positive clinical outcomes and telehealth, only that there were correlations.  This could have been better addressed by using Bayesian statistics in its methodologies or creating more differentiation in control groups or examining other possible explanations.  (Adam Steventon, 2012)

As a second example, in the second most-oft cited study regarding telehealth and diabetes, two key facts were overlooked: (1) all the subjects were from rural areas of South Carolina (which would have a higher prevalence of poverty and no formal education and associated life and dietary habits); and, (2) the subjects for intervention already had higher GHb such that any intervention that led toward normalcy would have falsely appeared more efficacious – meaning the control group was already nearly normalized before “traditional” intervention.  Improving patient experience of care is primary reason to use it. (Richard M. Davis, 2010)

According to historical studies, two communities have had higher rates of rejecting telehealth, African Americans and Native Americans.  African Americans were shown in a 2012 study to have lower confidence in confidentiality, privacy, and efficacy in the absence of a physical examination by a provider.  The control group was Latinos, instead of diverse populations, which may have skewed the results.  The authors attributed the higher rejection rates of telehealth by African Americans as a vestige of historic perceptions of government and social mistreatment. (Sheba George, 2012)  Similarly, the study indicating Native Americans distrusted government-backed telehealth was limited to veterans with mental health issues.  Because Native Americans have a long history of mistreatment by the US government, veterans perhaps more so, and mental health issues are an especially personal and intimate thing to talk about remotely, the study results pointing to a distrust of telehealth by Native Americans is dubious.  (Elizabeth Brooks, 2012)

Works Cited

Adam Steventon, M. B. (2012). Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial. BMJ, 344.

Beck, M. (2016, May 15). Study of Telemedicine Finds Misdiagnoses of Skin Problems: Online medical services are booming, but physicians remain concerned. The Wall Street Journal, pp. http://www.wsj.com/articles/study-of-telemedicine-finds-misdiagnoses-of-skin-problems-1463344200.

Cook, R. (2012). Exploring the benefits and challenges of telehealth. Nursing Times, 16-17.

Elizabeth Brooks, S. M. (2012). The Diffusion of Telehealth in Rural American Indian Communities: A Retrospective Survey of Key Stakeholders. Telemed J E Health, 60-66.

Richard M. Davis, A. D.-G.-D. (2010). TeleHealth Improves Diabetes Self-Management in an Underserved Community. Diabetes Care, 1712-1717.

Sheba George, A. H. (2012). How Do Low-Income Urban African Americans and Latinos Feel about Telemedicine? A Diffusion of Innovation Analysis. International Journal of Telemedicine and Applications, 1-9.

Moral Hazards in Medication Prescribing

Simply put, in behavioral economics, a moral hazard is the intersection between consumer preference and constraints that limit the consumers’ ability to exercise that preference (typically money or time). Traditionally, moral hazards were less common in health care because medical treatment choices were made exclusively by physicians; however, the advent of pharmaceutical advertising, and mass medical information accessibility via the Internet, have shifted choices to patients, allowing for moral hazards, depending upon who is paying. (Peter Zweifel, 2000)

While diagnostic studies patients’ desire, payment identity, and availability may create moral hazards, medication prescribing also allows moral hazards because patients use them heavily (volume), they are often expensive (cost), and patients may pay nominally; however, physicians still usually make these choices initially, which is importantly followed by the patients’ choices to fill the prescription, or whether to adhere to the treatment.

Providers knowing the costs of medications is of little value in preventing moral hazards unless four prerequisites are met: (a) they were taught the importance of value; (b) believe it to be true; (c) inquire to their patients whether cost is an issue (e.g., are they paying); and, (d) whether the provider cares if insurance companies or the government overpays.  Because of information ubiquity regarding medication prices (e.g., patients and providers can know costs with a Siri search on Google in seconds), formulary structures and operating methods play little to no role in moral hazards with prescriptions.  They are primarily a function of providers’ and patients’ attitudes about the payers. (Michael A. Fischer, 2006)

Works Cited

Michael A. Fischer, J. A. (2006). Educating Trainees about the Cost of Medications. AMA Journal of Ethics, 142-146.

Peter Zweifel, W. M. (2000). Moral Hazards & Consumer Incentives in Health Care. In J. N. A.J. Culyer, Handbook of Health Economics, Volume 1 (pp. 409-455). Amsterdam: Elsevier Science B. V .

The Ebay of Medicine

The bidding-for-services model is fatally flawed because it fails to have a mechanism for value, meaning the quality that one is getting for the price (think of Consumer Reports).  Therefore, its market is minimal to those who don’t have insurance, Medicare, or Medicaid, and don’t have much money.  People who pay for their own health care by choice are usually affluent and the rest are on private insurance, Medicare or Medicaid. Nor is the volume per registrant high.  One provider reports getting an average of two new, cash-paying patients per month. (Boodman, 2014)

My prediction is the bidding-for-services model will excel primarily with minimum-risk out-patient procedures that are commoditized.  Major illnesses and serious procedures are quality-focused for nearly every patient.  The for-profit and private Tennessee-based company that sponsors the Medibid Auctions notes that of the 6,000 registered providers, most are attempting to recruit foreign cash-paying patients.  Moreover, the quality risk is elevated by the fact that many, if not most, providers in Medibid are retail surgery centers with less public health reporting requirements (e.g., infection rates, etc.) than hospitals. (Boodman, 2014) 

Medibid is a race to the bottom of health care.  Patients are at higher risk because of lower quality care.  Payers are the second biggest losers because they may well be responsible for all the follow-on consequential complications of a low-quality low-cost procedure opted for by the patient.

Works Cited

Boodman, S. G. (2014, August 5). Medical auction website gives power to the patients to choose the lowest bidding surgeon. MedCity News: Health IT, pp. http://medcitynews.com/2014/08/medical-auction-website-gives-power-patients-choose-lowest-bidding-surgeon/.

Accountable Care Organizations: Panacea or Wishful Thinking?

In the last five years, the number of Accountable Care Organizations (ACOs) has increased from 100 to 700, which now serve 23 million Americans. These organizations are defined as they are formed, when a group of provider join forces to assume responsibility for the financial and quality ratio of services to a predesignated community, shifting responsibility for financial risks from payers to providers to incentivize value. (Tianna Tu, May 2015)

The creation of ACO’s is inextricably tied to Medicare.  The Centers for Medicare and Medicare Services (CMS) forerunner of ACO’s for Medicare was the Physician Group Practice Demonstration (PGP) pilot program that ran from 2005 to 2010 with mixed success.  Nevertheless, in December 2008, the Congressional Budget Office (CBO) was the idea champion for including ACOs in the Affordable Care Act of 2010.  Subsequently, CMS launched the Pioneer ACO program with Medicare in January 2012, and the Medicare Shared Savings Program (MSSP) in April 2012  (Tianna Tu, May 2015).

The success of these ACOs and their impact on Medicare are probably too new to show much results, make more complicated by six different types of ACOs, albeit the majority are three times: (1) physician group led (37%); (2) hospital led (28%); or, (3) both (35%). (Tianna Tu, May 2015)  Quantifying their efficacy or lack of efficacy is also complicated by the fact that Medicare ACOs and Medicaid ACOs are organized differently.  The former have value measurements, initially (Pioneer) are federal government sponsored, and issue reward based on a complex rubric; however, the Medicaid ACO’s are run by states by differing standards. (Tianna Tu, May 2015)

According to early indications, the Medicare ACOs (Pioneer and PGP) allege they have realized $877 million in medical expenses, with $460 million being the “cut” or share returned to the provider organizations. Results were inconsistent though because only 22%, for example, of the MSSP participants for Medicare ACOs qualified for any financial rewards. In the same year, Medicare ACO’s claimed to have improved quality in 28 to 30 of 32 scoring areas. (Tianna Tu, May 2015)

Analysis of these early results by two institutions, Leavitt Partners and the Brookings Institution indicate more complexity.  Specifically, they found correlation between cost savings and quality improvement was minimal and, Bayesian causal correlation was non-existent.  Moreover, they found that improving quality was substantially easier than reducing costs.  (Tianna Tu, May 2015)

Analyzing these findings from the patient, provider, and payer view would suggest the following: (1) for patients, there is insufficient information to show any positive or negative impact on the patients because quality improvements could have dealt with ACOs infrastructure and processes and not directly to patient care, and reduced costs could have meant less services to the patients, which could be negative; (2) for payers, their expenditures were ostensibly reduced by $877 million; however, no information is given as to whether that savings was passed on to patients to help them financially, or simply kept to increase medical insurers profits, retained earnings, or reserves; and, (3) for providers, well, it appears their getting the wool pulled over their eyes.  Their billable services were reduced by $877 million in exchange for a $460 million refund. In other words, they swapped $877 million for $460 million as a “reward” for nominal quality improvements, quite possibly, short-changing their patients in the process by diluting providers’ pure Hippocratic-oath patient focus to be tempted by dollars, and reducing patient services.  For the providers, it’s like the old joke that first prize is a week in Toledo, and second prize (less desirable) is two weeks in Toledo.  CMS is not giving providers an “incentive,” it’s punishing them by taking half their billings while possibly reducing patient care.  However, that’s not terribly surprising given the model originated with accounts at the CBO and not health care experts.

Works Cited

Tianna Tu, D. M. (May 2015). The Impact of Accountable Care: Origins and Future of Accountable Care Organizations. Washington, DC: Leavitt Partners.

Taking the Long View of Health Information Technology

It’s all about the timing.  There is no doubt that information technology has enormous potential to help health care in a myriad of ways, especially in creating data stores that exceed the capabilities of human memory, and processing power that exceeds human cognition.  (Masys, S33-41)  Further, successive collection of voluminous data creates an unprecedented circumstance in the volume of information, and the ability to mine it for knowledge and discovery. (Masys, S33-41)

For example, Medline, which record bibliographic entries for 4,500 journals, which as of 2002, consisted of 1.7 million records; however, was growing at a rate of 400,000 entries per year.  At that rate, if a practitioner read two articles per evening, it would take 550 years to get through the backlog – without consideration to the voluminous new entries. (Masys, S33-41)  Moreover, GenBank, the genetic sequence database run by the US National Institute of Health (NIH0, now has 211,423,912,047 base pairs and 193,739,511 gene sequences. (National Institute of Health, 2016)

Therefore, it is important to look beyond the month-to-month, and year-to-year, progresses and regresses of developing, deploying, and using health information technology and prematurely declaring whether or not HIT has met that goal as of today; that answer, is sure, healthcare has improved.  The more important question will become whether our own goals of creating and applying HIT will become a long-term detriment to health care from unmanageable data and knowledge that, once again, will exceed human cognition.

Works Cited

Masys, D. R. (S33-41). Effects Of Current And Future Information Technologies On The Health Care Workforce. Health Aff, 2002.

National Institute of Health. (2016, April 24). GenBank and WGS Statistics. Retrieved from NIH: http://www.ncbi.nlm.nih.gov/genbank/statistics/

The "Patient Protection & Affordable Care Act" & Health Information Technology

The primary differences between ambulatory and in-patient uses of health information technology are: (1) inpatient workflows are more complex than out-patient because of the complexity of multiple departments each with their own systems, staffing, and needs; (2) in-patient care is more labor intensive, the bulk of which is by medical assistants, who are the least trained and lowest paid in western healthcare; and, (3) inpatient facilities have different and more complex billing procedures than ambulatory settings. (Ryan, 2015)

The “Meaningful Use” provisions of the Patient Portability & Affordable Care Act attempting to use public policy to incentivize the creation and use of electronic health records (EHR), patient portals, and care coordination system in stages 1, 2, and 3, respectively, are creating neither higher quantity nor lower costs.  They may eventually create higher quality, in 5-10 years’ time, but at a substantially higher cost, which may push financially struggling health care providers to close rather than improve the quantity and quality of their care.

Analysis in a 2010 report by the global management consulting firm McKinsey & Company demonstrates that mandated EHR, and subsequent patient portals and care coordination systems, will have financially negative return on investment (ROI) for the foreseeable future.  McKinsey’s analysis uses a patient bed cost rubric wherein the average total cost to implement only the early stage EHR mandated by “meaningful use” is estimated at $80,000 to $100,000 (averaging $97,500) which after subsidies from the US government, could reduce the cost to $60,000 to $80,000 depending how early health care organizations implemented the technology.  Incentives decreased annually from $17,500 per bed in years 2011, 2012, and 2013, to $10,500 in 2014, $5,500 in 2015 and zero in 2016.  Meanwhile the financial penalty for choosing not to implement meaningful use began at $2,000 per bed in 2015 up to $35,000 per bed by 2019.  Their analysis went on to project long-term maintenance costs of $13,500 per bed (recurring annually) and benefits of $25,000 to $44,000 per bed. (Francois M. Laflamme, 2010)

Erroneous presumptions make EHR have a negative ROI for health care organizations.  First, assuming the annual maintenance cost is only $13,500 per bed, means the true penalty for choosing to ignore meaningful use is only $21,500 per year, which means organizations that waited to implement EHRs until 2015 won’t realize any financial return until 2020 at the earliest.  By that time, the engineered life of the EHR software will be exceeded and they will need to re-implement new software, meaning they never actually receive a positive ROI, only pay software and IT services companies in a never-ending implement-support-implement cycle.

Second, if one examines the type of costs involved in implementing an EHR system, many more of them are recurring expenses than $13,500 (13.5%).  While organizations may not pay the estimated $27,000 to $30,000 external consulting costs annually, they will need to pay the remaining costs on a recurring basis, which consist of: (a) $15-25,000 in hardware every three years; (b) $20-$22,000 in clinical software licenses annually; (c) $10-12,000 in training/re-training costs every 36 months presuming 30% turnover; (d) $5-6,000 in other annual software licenses; and, (e) $3-5,000 in additional internal IT support. (Francois M. Laflamme, 2010)  In total, the more probable annual support costs for the ERH are $40,834 per bed per year.  Therefore, from the annual support costs alone, health care organizations will arguably lose $5,834 per bed per year.  This fails to account for original capitalized cost of $80,000 to $100,000 per bed (average of 200 bed equaling $19.7 million per EHR).  Therefore, the government’s “meaningful use” mandates as applied to EHRs, are asking health care organizations to spend an average of $19.7 million every five years, then lose $1.167 million per year supporting it for five years, then repeat.

Ostensibly, the operational efficiency and clinical improvements organizations will gain from EHRs are estimated at $25,000 to $44,000 per bed.  However, examination of the analysis that calculates those numbers suggest the analysis is equally dubious.  For example, they estimate that organizations will save $8,000 to $15,000 per bed (32-34% of benefits) from EHRs preventing adverse drug events (ADEs). (Francois M. Laflamme, 2010)  The problem with that is that ADEs are a profit-center for health care delivery organizations, not a loss.  Whenever a patient has significant complications from an ADE, the provider must provide more and intensive treatments, for which they charge substantially.  Therefore, ADE reduction doesn’t’ save providers money, it saves payers money, and costs providers that money that payers would have needed to reimburse them. 

Second, McKinsey notes that another $20,000 per bed is projected to be saved from improving staffing efficiency. (Francois M. Laflamme, 2010)  That’s only true if there are infinitely more patients for the providers to see with the saved time – if there is a true opportunity cost.  Otherwise, staff may work more efficiently using EHRs; however, it doesn’t result in a less compensation for the organization, nor reduced, nor more billable hours, for the providers.

Therefore, it appears that the primary financial beneficiary of the government’s “meaningful use” mandate of technologies are the IT service and software companies realizing $120 billion per year implementing EHR systems, (Francois M. Laflamme, 2010) then redoing them every 3-5 years when the hardware and software changes.  The “meaningful use” mandates are, thereby, acting as financial incentives to the technology services and software industry more than health care organizations. I would argue that a better focus for public health care policy than incentivizing what is already one of the strongest technology markets in the world, would be the elements of for-profit and not-profit hospital conversions that are going awry.  Namely, mandating that health care organizations be barred from having “insiders” on their boards, only independent directors from different regions with expertise in health economics, delivery, and administration; and, prohibiting geographic monopolies or oligopolies of “non-profit” health care organizations, which reduce competition and thereby lower quality of care and increase prices.

Works Cited

Francois M. Laflamme, W. E. (2010, August). Reforming hospitals with IT investment. Retrieved from McKinsey & Company: http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/reforming-hospitals-with-it-investment

Kristin Harper, G. A. (2010). The Changing Disease-Scape in the Third Epidemiological Transition. Int J Environ Res Public Health, 675–697.

McKeown, R. E. (2010). The Epidemiologic Transition: Changing Patterns of Mortality and Population Dynamics. Am J Lifestyle Med, 19S-26S.

Robert York, K. K. (2016, January 6). Where Have All The Inpatients Gone? A Regional Study With National Implications. Health Affairs Blog, pp. http://healthaffairs.org/blog/2014/01/06/where-have-all-the-inpatients-gone-a-regional-study-with-national-implications/.

Ryan, J. (2015, December 15). Instructions for HIT: Inpatient vs Outpatient. Retrieved from Northwestern University 401: American Health Care System: https://canvas.northwestern.edu/courses/38753/pages/instructions-for-hit-inpatient-vs-outpatient