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DAMA Webinar - Big and Little Data Quality

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While technological innovation brings constant change to the data landscape, many organizations still struggle with the basics: ensuring they have reliable, high quality data. In health care, the promise of insight to be gained through analytics is dependent on ensuring the interactions between providers and patients are recorded accurately and completely. While traditional health care data is dependent on person-to-person contact, new technologies are emerging that change how health care is delivered and how health care data is captured, stored, accessed and used. Using health care as a lens through which to understand the emergence of big data, this presentation will ask the audience to think about data in old and new ways in order to gain insight about how to improve the quality of data, regardless of size.

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DAMA Webinar - Big and Little Data Quality

  1. 1. LITTLE DATA IN A BIG DATA WORLD The case of health care Dataversity / DAMA February 2016 Laura Sebastian-Coleman, Ph.D., IQCP Offered by: Connecticut General Life Insurance Company or Cigna Health and Life Insurance Company.
  2. 2. About me • Doing data quality in health care since 2003 • Have worked in banking, manufacturing, distribution, commercial insurance, and academia. • All have influenced my understanding of data, quality, and measurement • Developed the Data Quality Assessment Framework (DQAF); published in Measuring Data Quality for Ongoing Improvement (2013). • IAIDQ Distinguished Member Award 2015. • DAMA Publications Director, beginning summer 2015 • Influences on my thinking about data: • The challenge of how to measure data quality • The concept of measurement itself: A problems of measurement is a microcosm of the general challenge of data definition and collection. • The demands of data warehousing, especially of data integration. 2
  3. 3. • Abstract: – While technological innovation brings constant change to the data landscape, many organizations still struggle with the basics: ensuring they have reliable, high quality data. – In health care, the promise of insight to be gained through analytics is dependent on ensuring the interactions between providers and patients are recorded accurately and completely. – A lot of health care data is dependent on person-to-person contact. This fact influences its quality, as well as how it is captured, stored, accessed and used. – This presentation will ask the audience to think about data in old and new ways in order to gain additional insight about how to improve the quality of data, regardless of size. • Order of information – The promise of big data in health care vs. the reality of the health care system – The data quality in health care – How we think about data and data quality – Making good on the promise of health care analytics Abstract and Agenda 3Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  4. 4. • According to the blog Big Data Landscape, Big Data Health Care apps enable people and organizations to – Make well-informed decisions – Enhance patient engagement – Encourage prescription medication compliance – Adjust lifestyle choices – Maximize levels of well-being – Predict and prepare for individual illnesses – Manage demand within the health care system • In Health Care Delivery of the Future, Price Waterhouse Coopers describes a New Health Economy in which: – Digitally-enabled care is a fundamental business imperative. – There are major shifts in how care is being delivered. – Digital technology bridges time, distance and the expectation gap between consumers and clinicians. The Promise of Big Data in Health Care 4Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  5. 5. • At a recent insurance summit in Hartford, Connecticut, health care executives grappled with a quandary: “Only in health care does innovation lead to higher costs.” • Not only that, but within the health care system, there are inefficiencies. One executive estimated “Thirty cents on the dollar, at least, is not used effectively.” • Getting people to act in the best way for their health is “Very easy to say. Very hard to do.” The Reality of Health Care 5Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  6. 6. • The promise of health care analytics and big data depends on having high quality data. • Technology limitations – Data output from older, legacy claim adjudication systems differs greatly within and between organizations – Medical provider transition from paper to Electronic Health Records (EHR) is relatively recent and still underway – In 2008, only 4% of Providers had full EHR, 13% had a basic system • Lack of standards / standards implemented inconsistently • No standards for data quality Challenges with the Quality of Health Care Data 6Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  7. 7. • Goal: Improve knowledge of the quality of data gathered in non-clinical trial settings. – As research data warehouses and large scale data networks begin to become established sources of observational data, it becomes more critical that consistent methods for assessing and reporting data quality are developed and adopted so that users of data and consumers of results understand the potential impact of data quality on study results. • Factors that can cause misrepresentation of clinical events: – Inflexible systems – Coding practices – Gaps in standards • Example: Screening for high blood pressure in children – Clinicians were directed to screen children for high blood pressure. – After the initial screening, they continued to monitor some of the children. – When researchers looked at the data, it appeared children were increasingly at risk of high blood pressure. – Examination of records showed that the ‘increase’ was caused by incorrect use of hypertension diagnosis code – But there was not a diagnosis code for “considering hypertension” Academy Health Example 7Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  8. 8. • Person-to-person interactions between patients and health care providers • Interactions influence factors that may result in different interpretation of symptoms • Interactions are represented by data • Different choices in how to represent these interactions create different data stories Health Care data is about people 8Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  9. 9. Definition: Data • Data’s Latin root is dare, past participle of to give. Data means “something given.” In math and engineering, the terms data and givens are used interchangeably. • The New Oxford American Dictionary (NOAD) defines data as “facts and statistics collected together for reference or analysis.” • ISO defines data as “re-interpretable representation of information in a formalized manner suitable for communication, interpretation, or processing” (ISO 11179). • Observations about the concept of data – Data tries to tell the truth about the world (“facts”) – Data is formal – it has a shape – Data’s function is representational – Data is often about quantities, measurements, and other numeric representations “facts” – Things are done with data: reference, analysis, interpretation, processing • What the definitions leave out: – Data is made by people. We choose what characteristics to represent. The creation of data implies a set of expectations about data’s condition. – People also use data. The uses of data imply a set of expectations about data’s condition. 9
  10. 10. Science – Focuses on measurement as a means to create knowledge – Plans for data that is accurate and complete – Sets standards so that results are collected correctly and can be understood and reproduced – Tests and re-tests • Data is a PRODUCT of science. • High quality data is a necessity. Commerce – Creates measurements pragmatically to meet goals – Generates data by accident while executing its core processes • Data is a BY-PRODUCT of commerce • Historically, data quality has not an end in itself for commerce. Science, Commerce and Data Quality 10Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna • The History of Measurement = history of our ideas about data • Science & Commerce have different goals for and approaches to measurement and thus to data. • Our ideas about data come largely from science, but we create data based on commerce.
  11. 11. The Scientific Method vs. Commerce
  12. 12. Data Quality • Our ideas about data quality come largely from science, even though we create data based on commerce. • Today, we are using organizational data in scientific ways – to learn about our business. • We expect the data to be fit for this purpose, but we have not focused on ensuring representational effectiveness. 12 Fitness for purpose Representational effectiveness
  13. 13. • Even scientists are struggling with data quality in the age of Big Data. • A February 2015 article in Science News reported researchers are struggling to find insights as they sort through “mounds of data”: “Just keeping track of big data is a monumental undertaking. Sharing the data with other researchers, a critical piece of transparency and efficiency in science, has its own set of problems. And the tools used to analyze complex datasets are just as important as the data themselves. Each time a scientist chooses one computer program over another or decides to investigate one variable rather than a different one, the decision can lead to very different conclusions.” Data Quality and Big Data 13Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna • The inability to reproduce results has brought some studies into question. • The answer? Study the factors that influence reproducibility. • They have discovered a kind of “butterfly effect” in studies using big data: – Small changes in the initial conditions of an experiment can have significant effects on the outcome of replication attempts.
  14. 14. Butterfly effect in the clinical space A) Decision features a. Framing (e.g., gain vs. losses) (2 factors) b. Order of choices (e.g. A à B vs. BàA in a simple two choice decision) (2 factors) c. Choice justification (e.g., effect of regret, guilt etc. on dissonance reduction; yes vs. no) (2 factors) B) Situational factors a. Time pressure (e.g., yes vs no) (2 factors) b. Cognitive load (e.g., high vs. low) (2 factors) c. Social context (e.g., important vs. not important) (2 factors) C) Characteristics of decision-maker a. Individual [e.g., age (old vs. young), gender (female vs. male) (4 factors) b. Group (e.g, small vs. large group) (2 factors) c. Cultural factors (e.g., present vs. not preset/important) (2 factors) D) Individual differences a. Decision styles (e.g. intuitive vs. analytic) (2 factors) b. Cognitive ability (e.g., high vs. low) (2 factors) c. Personality (e.g., openness, conscientious, extraversion, agreeableness, neuroticism) (“Big 5” factors) Table 1. Minimum number of the factors affecting decision-making From Effect of Initial Conditions on Reproducibility of Scientific Research, by Benjamin Djulbegovic and Iztok Hozo • Small changes in the initial conditions of an experiment can have significant effects on the outcome of replication attempts. • Researchers used Doctor/Patient interactions to study the butterfly effect and identified 12 factors that influence clinical decision making. • Those initial factors make up 20,480 combinations that could represent the initial conditions of the experiment. • Yes, 20,480! Initial conditions can influence clinical decision making and the data that is recorded as part of it.
  15. 15. The butterfly effect takes on a whole new meaning 15Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  16. 16. • Recognize that there is variation within the health care system and use this variation to improve the system. • Reduce variation to simplify the system. • Recognize that data is a critical product of the health care system and plan for quality data. Making good on the promises of health care analytics 16Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  17. 17. • Variation may itself be meaningful. • For example, variation in how doctors make decisions – If we understand how doctors make decisions, and we understand he outcome of those decisions, then we can assess the results of those outcomes and provide that feedback to the health care system. • This is the basic promise of evidence-based medicine and health care analytics, but it requires a lot of data. Making good on the promises of health care analytics: Recognize variation 17Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  18. 18. • Establish and enforce data standards within health care – Including data quality standards • Collect more data through instruments, devices to reduce ambiguity of meaning – In 2015, PWC reported that 28% of consumers have at least one health related app on their tablet or smart phone. – Up from 13% just three years earlier. – About two-thirds of physicians reported that they would be willing to prescribe an app that would help patients manage a chronic disease. – Being able to integrate this kind of data into an electronic medical record represents an additional opportunity. • Enable feedback about the data from health care providers, patients, and other stakeholders – More health care data is being exposed directly to physicians and to consumers. – Physician compensation and Consumer incentives depend on this data. – To reduce costs and improve quality of care, health care companies will need means to respond to feedback and ensure data is accurate. Making good on the promises of health care analytics: Reduce variation 18Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  19. 19. • Recognize that improvement in health care and in quality of life depend on having trustworthy, reliable data. – Data is not secondary to successful outcomes, it is central to successful outcomes. • Focus from the start on ensuring the quality of that data, whether that data is little (such as a record of the facts of an office visit) or big (petabytes of biometric data collected from all the Fitbits in the USA). • High quality data is: – Clearly defined – Consistently collected through reliable processes – Presented through comprehensible standards • The processes and systems that we build to create this new health care data should be designed with the data itself in mind. – The industry as a whole must cease to create data as a by-product and recognize data as critical input to its primary goal: helping improve people’s health, well-being and sense of security. Making good on the promises of health care analytics: Plan for data as a product 19Confidential, unpublished property of Cigna. Do not duplicate or distribute. Use and distribution limited solely to authorized personnel. © 2015 Cigna
  20. 20. References • http://www.bigdatalandscape.com/blog/big-data-health-find-apps-can-improve-wellbeing • http://www.pwc.com/us/en/health-industries/top-health-industry-issues/assets/pwc-healthcare-delivery-of- the-future.pdf • http://touch.courant.com/#section/-1/article/p2p-84932278/ • https://en.wikipedia.org/wiki/Electronic_Medical_Record#United_States • http://www.academyhealth.org/ • http://www.pcori.org/about-us/why-pcori-was-created • http://courses.washington.edu/geog482/resource/14_Beyond_Accuracy.pdf • https://www.sciencenews.org/article/redoing-scientific-research-best-way-find-truth • https://www.sciencenews.org/article/big-data-studies-come-replication-challenges • http://www.scopemed.org/fulltextpdf.php?mno=162185 • http://consumerhealthchoices.org/wp-content/uploads/2015/05/HealthDataGuide-June2015.pdf • http://www.pwc.com/us/en/health-industries/top-health-industry-issues/assets/pwc-healthcare-delivery-of- the-future.pdf • http://kff.org/health-reform/fact-sheet/summary-of-the-affordable-care-act/ • http://www.forbes.com/sites#/sites/emc/2014/01/22/can-big-data-and-mobile-make-health-care-more- effective/ • http://www.nytimes.com/2016/01/17/opinion/sunday/how-measurement-fails-doctors-and- teachers.html?_r=0 •
  21. 21. All Cigna products and services are provided exclusively by or through operating subsidiaries of Cigna Corporation, including Cigna Health and Life Insurance Company, Connecticut General Life Insurance Company, Cigna Behavioral Health, Inc., and HMO or service company subsidiaries of Cigna Health Corporation. The Cigna name, logo, and other Cigna marks are owned by Cigna Intellectual Property, Inc. 000000 00/15 © 2015 Cigna. Some content provided under license.

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