On the Wisdom of Crowds:

Collective Predictive Analytics

“All great lies have a seed of truth” (James Cottrell, personal communication, 2004). In 1907, Sir Francis Galton (1855-1911) – a British statistician whose body of research focused on human intelligence and who also happened to be Charles Darwin’s cousin – observed that in a festival contest in Cornwall, where people attempted to guess the weight of an ox, the average of all guesses was consistently close to the ox’s actual weight (Galton, 1907; Ball, 2014; Gega, 2000). Author James Surowiecki resurrected this observation for his book, The Wisdom of Crowds, in 2005 (Surowiecki, 2005).

While Galton is often considered the “father” of modern collective intelligence based on a mean, his 1907 publication in the journal Nature focused on how well crowds predicted the median because it was subject to less error – or more confidence in its interval. The mean was within one pound; the median was within nine pounds, leading Galton to answer with more specificity to a reader’s letter that, because in small sets the exclusion of a measurement could greatly alter the mean and impact the median much less, he found collective intelligence most applicable for predicting median ranges rather than means (Galton, Letters to the editor: The ballot box, 1907). However, recent re-examinations of Galton’s data indicate that errors in the original calculations such that the 800-strong crowd’s estimate of the mean was not within one pound, but the exact weight of the ox (Wallis, 2014).

This theory of “collective intelligence” has been studied and analyzed for the 110 subsequent years to try and validate how, when, and in what circumstances it accurately and inaccurately predicts. Critics of Surowiecki’s popularizing review of collective intelligence point out crowds’ skill at optimizing while being less skilled at innovation or creativity (Lanier, 2010). Moreover, collective intelligence is poor at defining the right question – often the most important aspect of inquiry – and scalar results (Lanier, 2010).

One example of collective or aggregated intelligence’s predictive failure is in economic measurements. Experts who opine on measurements of economic growth are often incorrect and, moreover, this error is compounded because the prediction sets an expectation that, when missed, causes negative reactions in financial markets (Cassino, 2016). The failing of collective predictive intelligence in financial markets may largely be to this double-action – they set an expectation and the inaccuracy relative to the expectation makes markets respond disproportionately. While the origins and dynamics of irrational markets and “black swan events” are beyond the author’s scope here, these failings of collective intelligence may be an early causal event.

Other evidence suggests that collective intelligence can be effective creatively; however, mostly when applied to brainstorming-type activities among a cohort of experts. For example, in a 2011 contest in the Harvard Medical School community, 40,000 faculty, staff, and students competed to derive the most important questions – what is unknown but needed to be known – to cure type 1 diabetes, with impressive results (Harvard Medical School, 2011).

For now, the lessons learned regarding collective intelligence appear to be this: it is best when an intellectually diverse cohort of experts answer a predefined question and focuses on optimization around medians or brainstorming (Ball, 2014). It is least best when the crowd thinks the same, includes many non-experts, faces a spectrum or scalar of answers, and whose work product is not the basis for many future decisions on which error could be compounded.

Works Cited

Ball, P. (2014, July 8). 'Wisdom of the crowd;' myths & realities. BBC: Future, pp. http://www.bbc.com/future/story/20140708-when-crowd-wisdom-goes-wrong.

Cassino, D. (2016, July 8). The ‘wisdom of the crowd’ has a pretty bad track record at predicting jobs reports. Havard Business Review, pp. https://hbr.org/2016/07/the-wisdom-of-the-crowd-has-a-pretty-bad-track-record-at-predicting-jobs-reports.

Galton, F. (1907). Letters to the editor: The ballot box. Nature, http://galton.org/cgi-bin/searchImages/galton/search/essays/pages/galton-1907-ballot-box_1.htm.

Galton, F. (1907). Vox populi. Nature, 450-451.

Gega, S. (2000, May). Sir Francis Galton. Retrieved from Muskingham College: http://muskingum.edu/~psych/psycweb/history/galton.htm

Harvard Medical School. (2011, April 6). The wisdom of crowds: Contest yields innovative strategies for conquering Type 1 diabetes. Retrieved from Harvard Medical School: https://hms.harvard.edu/news/wisdom-crowds-4-6-11

Lanier, J. (2010). You are Not a Gadget: A Manifesto. London: Allen Lane.

Surowiecki, J. (2005). The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations,. New York City: Anchor Books.

Wallis, K. (2014). Revisiting Francis Galton's forecasting competition. Statistical Science, 420-424.

Big Data will Slow, Not Accelerate, Discovery

The generations of us living from 1984 to 2020 are witnessing the largest and most rapid cycle of creation since the Big Bang.  That is, one 36-year window that is competing with the 14.5 billion years of existence that humans know of. This creation guides what information is available to us, our options, and our choices in every area of life, thousands of times every day.  It can determine life or death, the transference of wealth, the geopolitics of Earth, and the expansion of knowledge.  We are its creators, and we can neither stop its creation nor know it in any tangible way.  It is:  data.

So, what happened?  How did this come about? What made Big Data, big?  It has largely come about because of the ubiquity of processors in the late 20^th century, the creation of and growth of users of the Internet (from 1,000 in 1984 to 2.7 billion in 2016, half of whom are on Facebook), then miniaturization of processors into smart devices like phones, watches, etc.; however, mostly now, the exponential growth of data is the result of unstructured databases (e.g., MongoDB, NoSQL, etc.) to hold all our digital interactions and behaviors.  This unstructured behavioral data currently accounts for about 75% of the data being created.  But, we haven’t seen anything yet because the growth of data will explode even more over the next 15 years from the Internet-of-Things. (Wall, 2014)

So, how big is “Big?”  According to IBM, in 2012, we created 2.5 gigabytes of data per day.  At this rate, in another much-cited statistic by IBM, which was originally written by Norwegian, Ase Dragland from think-tank SINTEF in 2013, 90% of all data in the world was created in the prior two years. (Dragland, 2013) However, this data growth rate – the speed of creation – is actually accelerating.  According to research data scientist Richard Ferres from the Australian National Data Service (ANDS), we are creating data 10x faster every two years. (Ferres, 2015) In other words, starting from 1 in 1985, we were at a speed of 1x10¹⁵ in 2015 (e.g. one quadrillion “miles per hour”), and in 2017, our speed of data creation is 1x10¹⁶ (e.g., ten quadrillion “miles per hour”).

If that acceleration wasn’t fast enough, we’re soon going to be creating data a lot faster still, because of the Internet-of-Things (IoT).  The IoT is the collective name for the billions of devices that are being embedded with sensors to communicate data in networks – think of the “smart” refrigerator by Samsung that tracks what groceries are inside it, or the car or home alarm system or baby monitor that you can control with your mobile phone. Technology research group Gartner estimates there were 6.7 billion such devices or sensors on-line in 2016, (Gartner, 2015) and competitor research group IDC estimates there will be 30 billion by 2020. (IDC, 2014) Recall, there are approximately 2.7 billion Internet users.  So, the prediction is the number of data creators will increase by 10x within the next three years alone.  Simplistic math would suggest that 10x the number of data creators accelerating at 10x every two years may mean that within 3-4 years, the speed of our annual data creation will accelerate 100x every two years.

But these numbers are at such a scale as to make them difficult for human brains to understand or imagine.  Two gigabytes, which was 80% of the amount of data we collectively created in 2012 every day, is about 20 yards (60 feet) of average-length books on a shelf, or about 6.67 kilometers (4.15 miles) of books per year.  But because we created data 10x faster in 2016, it means we were up to 66.7 kilometers (41.5 miles) of books face-to-back per year.  If we accelerate 10x faster by 2018, as predicted, and 100x faster as suggested above by 2020, that would mean we will create 667 kilometers (415 miles) of books on a shelf in 2018, and will be creating 6,667 kilometers (4,150 miles) of books on a shelf, every year, by 2020. At this rate, if we were publishing books instead of electronic data, it would be enough to encircle the Earth at the equator every year sometime before 2022 – only six years away.

Imagine if meaningful knowledge or discovery in Big Data were diamonds in the Earth.  To mine or find them, we have to collect tens of thousands of cubic yards of soil.  Then, someone comes along with an invention that enables us to collect billions of cubic yards of soil premised on the theory that we will find orders of magnitude more diamonds in orders of magnitude more dirt.  Maybe.  But, for sure, it makes the mission of diamond miners (data scientists to us) orders of magnitude harder too.

Worse yet, unless and until we become proficient at its use, big data statistics often creates more false knowledge than true knowledge.  The most common thing a researcher does in trying to discover meaningful new relationships in this data is calculate correlations (e.g., every time X changes, Y also changes); however, these correlations are often “false” because we presume they are causal (X changing causes Y to change) leading to misinformation.  To determine a causal relationship requires Bayesian statistics, a rather advanced statistical toolbox with which many data scientists, let alone executive decision makers, are unfamiliar with.  However, the error-prone process doesn’t end there because there are two major categories of Bayesian statistics – naïve (assuming data points function independent from each other) and network (assuming data points influence each other).  If and when a data scientist is familiar with Bayes, 50% of the time they use the wrong application of the formula. The bottom line being that most of the types of correlations and basic statistics that people initially apply to Big Data give false or misleading information.

In our analogy, not only is our speed of creating Big Data increasing at an increasing rate, meaning we have to move millions of cubic yards of soil one year, billions the second year, and tens of billions the third year, the soil we’re “processing” is riddled with fake diamonds.  While often mistakenly attributed to physicist Stephen Hawking or US Librarian of Congress, Daniel Boorstin, it was actually historian Henry Thomas Buckle, in the second volume of his 1861 series “History of the Civilization of England” who first observed that: “the greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.” (Buckle, 1861)

We don’t necessarily need bigger data, although we’re certainly going to get it.  We need more meaningful data.  Therefore, the exponential amassing of data that is underway at an unprecedented rate in the history of humankind, and is about to accelerate even more, is creating more noise to sift through to find meaningful knowledge then before the Big Data era.  It is becoming harder to identify what is important.  The evolution of humankind via the discovery of knowledge, therefore, will be accelerated not by the gluttonous creation of ever-bigger data but by focusing on the most meaningful data and creating and sequestering that.

Works Cited

Buckle, H. T. (1861). An Examination of the Scotch Intellect During the 18th Century. In H. T. Buckle, History of the Civilization of England (p. 408). New York: D. Appleton & Co.

Dragland, A. (2013, May 22). Big Data - For Better or Worse. Retrieved from SINTEF: www.sintef.no/en/latest-news/

Ferres, R. (2015, July 14). The Growth Curve of Data. Australia: Quora.

Gartner. (2015, November 10). Gartner Says 6.4 Billion Connected "Things" Will Be in Use in 2016, Up 30 Percent From 2015. Retrieved from Gartner: http://www.gartner.com/newsroom/id/3165317

IDC. (2014, April). The Digital Universe of Opportunities: Rich Data & The Increasing Value of the Internet-of-Things. Retrieved from IDC - EMC: https://www.emc.com/leadership/digital-universe/2014iview/internet-of-things.htm

Wall, M. (2014, March 4). Big Data: Are You Ready for Blast-Off? BBC News, pp. www.bbc.com/news/business-26383058.

DHHS Clinical Guidelines Are Harming Patients 5-24% of the Time

This article summarizes two randomly-selected clinical guidelines and juxtaposes each in a tabular format to the 23-point rubric published by the Appraisal of Guidelines for Research & Evaluation (AGREE) organization. Both guidelines are searchable in ontology-type categories relative to the audience they pertain to (e.g., age ranges, bodily systems, etc.). There is no sorting-type mechanism that would allow providers to find the relevant portion of the guidelines sought after out of the dozens of pages of each “guideline.”

In both cases, the National Guideline Clearinghouse maintained by the US Department of Health and Human Services was found to be vastly inferior to using the commercial service UpToDate, largely because of the frequency of updating. A cursory review of the guidelines in the US Government clearinghouse indicated many guidelines were more than several years old. In a 2014 study published in the journal CMAJ evaluating the survival validity (e.g., how long guidelines were valid without updating) found that 5% of guidelines were invalid in one year, 14% were invalid after two years, 19% were invalid after three years, and 22% were invalid after four years. (Laura Martínez García, 2014) In other words, a significant portion, if not a majority, of the clinical guidelines being propagated by the US Department of Health & Human Services are now invalid and may actually harm, not help, patients. Conversely, UpToDate has hundreds of thousands of researchers and physicians peer-reviewing entries frequently to ensure it is “up to date.”

The clinical guideline for the treatment of chronic hepatitis B (NGC: 010903) is a 45-page standard written by the American Association for the Study of Liver Diseases in January 2016. This clinical standard follows a format that is similar upon first impression, but different in quality of content, than the example for adult sinusitis. It begins with a general summary of treatment (e.g., specific anti-viral therapy medication combinations). It continues by differentiating recommended recovery treatments for adults if they are certain co-morbidities (e.g., viremia, pregnancy, etc.) or are non-responsive to first-line medications. It follows a format with a section for recommended algorithms (none), risk assessing, methodologies in its design (e.g., similar to study design methodologies), and a 15 outcome of treatment considerations. (American Association for the Study of Liver Diseases, 2017)

The clinical guideline for the treatment of adult sinusitis (NGC: 010703) is a 70-page standard written by the American Academy of Otolaryngology and Head and Neck Surgery Foundation in September 2007, and revised in April 2015. The standard is broken down into four sections to be performed in sequence: (1) differential diagnoses (e.g., acute bacterial rhinosinusitis (ABRS)); (2) symptomatic relief goals; (3) medication choice (e.g., amoxicillin); and, (4) recovery therapies (contingencies if primary treatment recommendations fail). While the sinusitis standard has the same categories of description as the standard for Hepatitis B, the answers are often perfunctory and lack much development or details. (American Academy of Otolaryngology - Head and Neck Surgery Foundation, 2017)

Works Cited

American Academy of Otolaryngology - Head and Neck Surgery Foundation. (2017, January 8). Clinical practice guideline (update): adult sinusitis. Retrieved from US DHHS: AHRQ National Guideline Clearninghouse: https://www.guideline.gov/summaries/summary/49207/clinical-practice-guideline-update-adult-sinusitis

American Association for the Study of Liver Diseases. (2017, January 8). AASLD guidelines for treatment of chronic hepatitis B. Retrieved from US DHHS: AHRQ - National Guideline Clearinghouse: https://www.guideline.gov/search?f_Clinical_Specialty=Infectious+Diseases&fLockTerm=Infectious+Diseases&f_Meets_Revised_Inclusion_Criteria=yes&page=1

Laura Martínez García, A. J. (2014). The validity of recommendations from clinical guidelines: a survival analysis. CMAJ, 1211–1219.

National Quality Forum. (2017, January 8). Abdominal Aortic Aneurysm (AAA) Repair Mortality Rate (IQI 11). Retrieved from National Quality Forum: http://www.qualityforum.org/QPS/QPSTool.aspx#qpsPageState=%7B%22TabType%22%3A1,%22TabContentType%22%3A2,%22SearchCriteriaForStandard%22%3A%7B%22TaxonomyIDs%22%3A%5B%2216%3A389%22%5D,%22SelectedTypeAheadFilterOption%22%3Anull,%22Keyword%22%3A%22%22,%22Page

NQF. (2017, January 8). Accidental Puncture or Laceration Rate (PDI #1). Retrieved from National Qualify Forum: http://www.qualityforum.org/QPS/QPSTool.aspx#qpsPageState=%7B%22TabType%22%3A1,%22TabContentType%22%3A2,%22SearchCriteriaForStandard%22%3A%7B%22TaxonomyIDs%22%3A%5B%2216%3A389%22%5D,%22SelectedTypeAheadFilterOption%22%3Anull,%22Keyword%22%3A%22%22,%22Page

The Unintended Consequences of Pay-for-Performance Healthcare

While pay-for-performance (P4P) is logical and all the rage, there is a contrarian view upon a deeper, more critical analysis. From that analysis, three concerns come to mind: (1) can incentives cause a reduction in intrinsic motivations; (2) how often, when, and why do incentives lead to abuse and corruption; and, (3) why do we (and the government) assume that providers who chose an altruistic career with a relatively low return on investment for the amount of training necessary in time and money would be susceptible to financial incentives anyway?

In a 2013 study published in the journal Health Psychology, researchers examined the ability for financial “incentives to undermine or ‘crowd out’ intrinsic motivation.” Ironically, it found that financial incentives for improved healthcare behavior did not interfere or “crowd out” intrinsic motivation; however, only because the intrinsic motivation of patients who were being financially incentivized was so low to start with. However, this result suggests the converse may be true. Namely, that those with a high intrinsic motivation (e.g., providers) may have this intrinsic altruistic motivation “crowded out” by financial incentives. (Marianne Promberger, 2013)

Moreover, any financial incentive to coerce providers to behave in a certain way can cross a free-will Rubicon wherein once they agree to modify their treatment in exchange for money, the providers are being controlled by the incentive instead of their best natural medical judgment. The New York Times did an examination of this very issue in 2014, wherein they coined the term “moral licensing,” which they described as when “the physician is able to rationalize forcing or withholding treatment, regardless of clinical judgment or patient preference, as acceptable for the good of the population.” (Pamela Hartzband, 2014)

Finally, as the author has noted in other writing for Northwestern University, arguably the most comprehensive meta-analysis of pay-for-performance in healthcare, conducted by the Rand Corporation in 2016 examining 49 studies published in peer-reviewed journals found that pay-for-performance had minimal impact to improve the quality of care. (Cheryl Damberg, 2016) While that may be because institutions choose the wrong metrics to measure, or definitions, or baselines for comparisons, it may also just be that providers took their Hippocratic Oath seriously and, largely, try to stay focused on acting in each patient’s best interests without outside interference, coercion, or influence.

One viable and promising solution in the form of a new standard of care is decision-support systems. While their existence has been around for decades, improved processing power, artificial intelligence, cloud computing, the Internet-of-Things, and patient-generated mhealth data create a confluence wherein purely objective standards of care can be recommended for every patient. Beyond decision support systems, but using their systems and methods, is personalized genomic medicine. One strategic goal of our work at Bioinformatix’ Rx&You is to collect Total Satisfaction Quality (TSQ) data from large cohorts of patients in regard to the efficacy of their medication regimens. This information, when combined with their adverse event history, cost data, and patients’ genomic variants we believe can create a “Codex” via comparative effectiveness research (e.g., which medications work best for whom and when, which are the most dangerous, which are the best value).

Works Cited

Cheryl Damberg, M. S. (2016). Measuring Success in Health Care Value-Based Purchasing Programs. Santa Monica, CA: RAND Corporation.

Marianne Promberger, T. M. (2013). When Do Financial Incentives Reduce Intrinsic Motivation? Comparing Behaviors Studied in Psychological and Economic Literatures. Health Psychol, 950–957.

Pamela Hartzband, J. G. (2014, November 18). How Medical Care is Being Corrupted. The New York Times, pp. https://www.nytimes.com/2014/11/19/opinion/how-medical-care-is-being-corrupted.html?_r=0.

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.