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The HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) survey is the first national, standardized, publicly reported survey of patients' perspectives of hospital care. HCAHPS (pronounced "H-caps"), also known as the CAHPS Hospital Survey, is a survey instrument and data collection methodology for measuring patients' perceptions of their hospital experience. While many hospitals have collected information on patient satisfaction for their own internal use, until HCAHPS there was no national standard for collecting and publicly reporting information about patient experience of care that allowed valid comparisons to be made across hospitals locally, regionally and nationally.

Three broad goals have shaped HCAHPS. First, the survey is designed to produce data about patients' perspectives of care that allow objective and meaningful comparisons of hospitals on topics that are important to consumers. Second, public reporting of the survey results creates new incentives for hospitals to improve quality of care. Third, public reporting serves to enhance accountability in health care by increasing transparency of the quality of hospital care provided in return for the public investment. With these goals in mind, the Centers for Medicare & Medicaid Services (CMS) and the HCAHPS Project Team have taken substantial steps to assure that the survey is credible, useful, and practical.

HCAHPS Content and Administration

The HCAHPS survey asks discharged patients 27 questions about their recent hospital stay. The survey contains 18 core questions about critical aspects of patients' hospital experiences (communication with nurses and doctors, the responsiveness of hospital staff, the cleanliness and quietness of the hospital environment, pain management, communication about medicines, discharge information, overall rating of hospital, and would they recommend the hospital). The survey also includes four items to direct patients to relevant questions, three items to adjust for the mix of patients across hospitals, and two items that support Congressionally-mandated reports.

The HCAHPS survey is administered to a random sample of adult patients across medical conditions between 48 hours and six weeks after discharge; the survey is not restricted to Medicare beneficiaries. Hospitals may either use an approved survey vendor, or collect their own HCAHPS data (if approved by CMS to do so). HCAHPS can be implemented in four different survey modes: mail, telephone, mail with telephone follow-up, or active interactive voice recognition (IVR). Hospitals can use the HCAHPS survey alone, or include additional questions after the core HCAHPS items. Hospitals must survey patients throughout each month of the year. The survey is available in official English, Spanish, Chinese, Russian and Vietnamese versions. The survey and its protocols for sampling, data collection and coding, and file submission can be found in the current HCAHPS Quality Assurance Guidelines, which is available on the official HCAHPS website, www.hcahpsonline.org.

HCAHPS Development, Testing and Endorsement

Beginning in 2002, CMS partnered with the Agency for Healthcare Research and Quality (AHRQ), another agency in the federal Department of Health and Human Services, to develop and test the HCAHPS survey. AHRQ carried out a rigorous scientific process, including a public call for measures; review of literature; cognitive interviews; consumer focus groups; stakeholder input; a three-state pilot test; extensive psychometric analyses; consumer testing; and numerous small-scale field tests. During this process, CMS provided three opportunities for the public to comment on HCAHPS, and responded to well over one thousand comments.

In May 2005, the HCAHPS survey was endorsed by the National Quality Forum, a national organization that represents the consensus of many healthcare providers, consumer groups, professional associations, purchasers, federal agencies, and research and quality organizations. In December 2005, the federal Office of Management and Budget gave its final approval for the national implementation of HCAHPS for public reporting purposes. CMS implemented the HCAHPS survey in October 2006, and the first public reporting of HCAHPS results occurred in March 2008. The survey, its methodology and the results it produces are in the public domain.

Hospitals implement HCAHPS under the auspices of the Hospital Quality Alliance (HQA), a private/public partnership that includes major hospital and medical associations, consumer groups, measurement and accrediting bodies, government, and other groups that share an interest in improving hospital quality. The HQA has endorsed HCAHPS.

The enactment of the Deficit Reduction Act of 2005 created an additional incentive for acute care hospitals to participate in HCAHPS. Since July 2007, hospitals subject to the Inpatient Prospective Payment System (IPPS) annual payment update provisions ("subsection (d) hospitals") must collect and submit HCAHPS data in order to receive their full IPPS annual payment update. IPPS hospitals that fail to publicly report the required quality measures, which include the HCAHPS survey, may receive an annual payment update that is reduced by 2.0 percentage points. Non-IPPS hospitals, such as Critical Access Hospitals, may voluntarily participate in HCAHPS.

The Patient Protection and Affordable Care Act of 2010 (P.L. 111-148) includes HCAHPS among the measures to be used to calculate value-based incentive payments in the Hospital Value-Based Purchasing program, beginning with discharges in October 2012.

HCAHPS and Public Reporting

Publicly reported HCAHPS results are based on four consecutive quarters of patient surveys. CMS publishes participating hospitals' HCAHPS results on the Hospital Compare website (www.hospitalcompare.hhs.gov) four times a year, with the oldest quarter of patient surveys rolling off as the most recent quarter rolls on. A downloadable version of HCAHPS results is also available through this website. Additional HCAHPS results can be found on HCAHPS On-Line, (www.hcahpsonline.org).

To ensure that publicly reported HCAHPS scores allow fair and accurate comparisons across hospitals, it is necessary to adjust for factors that are not directly related to hospital performance but which affect how patients answer HCAHPS survey items. These adjustments eliminate any advantage or disadvantage in scores that might result from the survey mode employed or from characteristics of patients that are beyond a hospital's control. In addition, the HCAHPS Project Team engages in a series of quality oversight activities, including inspection of survey administration procedures, statistical analyses of submitted data, and site visits of HCAHPS survey vendors and self-administering hospitals, to assure that the HCAHPS survey is being administered according to the protocols.

For More Information

To learn more about HCAHPS, including background information, recent news and policy updates, please visit HCAHPS On-Line, at www.hcahpsonline.org. Or access the Downloads and links below.

Note: CAHPS (Consumer Assessment of Healthcare Providers and Systems) is a registered trademark of the Agency for Healthcare Research and Quality, a U.S. Government agency.

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HCAHPS: Patients' Perspectives of Care Survey

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The HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) survey is the first national, standardized, publicly reported survey of patients' perspectives of hospital care. HCAHPS (pronounced "H-caps"), also known as the CAHPS Hospital Survey, is a survey instrument and data collection methodology for measuring patients' perceptions of their hospital experience.

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Hospitals need to step up to the plate and take on the responsibility of educating nursing staff on the what exactly HCAHPS surveys measure and what they can do to improve scores. This process shouldn't be a monthly blame-session in which the entire department feels defeated, but instead a continuous process that begins at orientation and is evaluated routinely.

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Overview

There is a great demand today for accurate, useful information on health care quality that can inform the decisions of consumers, employers, physicians and other clinicians, and policymakers. This is increasingly important as the health care system moves towards value-based reimbursement models.

It is difficult to have actionable and useful information because physicians and other clinicians must currently report multiple quality measures to different entities. Measure requirements are often not aligned among payers, which has resulted in confusion and complexity for reporting providers.

To address this problem, the Centers for Medicare & Medicaid Services (CMS), commercial plans, Medicare and Medicaid managed care plans, purchasers, physician and other care provider organizations, and consumers worked together through the Core Quality Measures Collaborative to identify core sets of quality measures that payers have committed to using for reporting as soon as feasible. The guiding principles used by the Collaborative in developing the core measure sets are that they be meaningful to patients, consumers, and physicians, while reducing variability in measure selection, collection burden, and cost. The goal is to establish broadly agreed upon core measure sets that could be harmonized across both commercial and government payers.

Core Quality Measures

The Core Quality Measure Collaborative, led by the America's Health Insurance Plans (AHIP) and its member plans' Chief Medical Officers, leaders from CMS and the National Quality Forum (NQF), as well as national physician organizations, employers and consumers, worked hard to reach consensus on core performance measures. Through the use of a multi-stakeholder process, the Collaborative promotes alignment and harmonization of measure use and collection across payers in both the public and private sectors.

Designed to be meaningful to patients, consumers, and physicians, the alignment of these core measure sets will aid in:

promotion of measurement that is evidence-based and generates valuable information for quality improvement,

consumer decision-making,

value-based payment and purchasing,

reduction in the variability in measure selection, and

decreased provider's collection burden and cost.

CMS believes that by reducing burden on providers and focusing quality improvement on key areas across payers, quality of care can be improved for patients more effectively and efficiently.

To develop the core measure sets the Collaborative split into workgroups and reviewed measures currently in use by CMS and health plans as well as measures endorsed by NQF for the individual measure sets. Based on this review and discussion the workgroups identified a consensus core set for the selected clinical areas. This consensus core set was further discussed by all Collaborative members before being finalized. Additionally, the Collaborative developed a framework of aims and principles that informed the selection of core measure sets.

The core measures are in the following eight sets:

Accountable Care Organizations (ACOs), Patient Centered Medical Homes (PCMH), and Primary Care

Cardiology

Gastroenterology

HIV and Hepatitis C

Medical Oncology

Obstetrics and Gynecology

Orthopedics

Pediatric

CMS is already using measures from the each of the core sets. Using the notice and public comment rule-making process, CMS also intends to implement new core measures across applicable Medicare quality programs as appropriate, while eliminating redundant measures that are not part of the core set. Commercial health plans are rolling out the core measures as part of their contract cycle.

The Core Quality Measures Collaborative views the upcoming year as a transitional period, as it begins adoption and harmonization of the measures. Ongoing monitoring by the Collaborative of the use of these measures will enable modifications of measure sets, as needed and based on lessons learned, including minimizing unintended consequences and selection of new measures as better measures become available.

NEW: The Core Quality Measure Collaborative' s pediatric measure set is intended for measurement at the health care provider and group practice levels. Because sever of the nine measures in the Core Quality Measure Collaborative (CQMC) pediatric set are also in the Medicaid and CHIP Child Core Set , reporting on these measures at the provider and practice levels may also help to encourage reporting on the state-level Child Core Set measures.

Get Involved!

CMS will go through a public notice and comment rule-making for implementation of these core sets and looks forward to public input on the measures included in these core measure sets. As comment periods open, this page will be updated.

These seven core measure sets are a major step forward for alignment of quality measures between public and private payers and provides a framework upon which future efforts can be based.

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There is a great demand today for accurate, useful information on health care quality that can inform the decisions of consumers, employers, physicians and other clinicians, and policymakers. This is increasingly important as the health care system moves towards value-based reimbursement models.

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Patient-reported outcome (PRO) data are defined by the FDA as "any report of the status of a patient's health condition that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else." These data are increasingly used to inform and guide patient-centered care, clinical decision-making, and health policy decisions and are an important component of many of the Collaboratory's Demonstration Projects.

The PRO Core works closely with the Collaboratory to create guidelines and define best practices with respect to

Selecting, compiling, and curating the most appropriate PRO measures (and stimulating the development of new instruments when new solutions are needed)

Creating efficient, high-quality PRO data collection systems compatible with electronic health records and registries

Conducting statistical analyses of PRO endpoints

As part of this work, the Core conducts interviews with clinicians and other investigators using PROs, including Demonstration Project principle investigators, to determine how PROs are currently used in the field, brainstorm solutions to challenges, and monitor progress with PRO implementation and data collection.

The Core has written a PROs Living Textbook chapter, a white paper Strategies for Overcoming Barriers to Patient-Reported Outcomes Measures, and a chapter on including PROs in registries as a part of the AHRQ handbook on registries. It also helped lead the development of an Effectiveness Guidance Document (EGD) for incorporating PROs into comparative effectiveness research in adult oncology and held a workshop to help clinicians implement the NIH Patient Reported Outcomes Measurement Information System (PROMIS). Upcoming publications include a Practical Guide to Initiating and Implementing Patient-Reported Outcomes Measures.

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Patient-Reported Outcomes - Rethinking Clinical Trials

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Chair: Kevin Weinfurt NIH Representatives: Susan Czajkowski, William Riley Members: Arne Beck, M. Fernanda Bellolio, Richard Deyo, Janna Friedly, Erik Hess, Tamara Isakova, Francis Keefe, Chris Knoepke, Margaret Kuklinski, Amy Loree, Dana Miskulin, Bret Moran, Ashli Owen-Smith, Pamela Peterson, Stacy Sterling, Jon Tilburt, James Tulsky, Doug Zatzick Project Manager: MariJo Mencini Products and Publications | ...

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HCAHPS (the Hospital Consumer Assessment of Healthcare Providers and Systems) is a patient satisfaction survey required by CMS (the Centers for Medicare and Medicaid Services) for all hospitals in the United States.

In the survey, patients provide feedback on their experience with a hospital in specific areas, including:

Communication with Nurses

Communication with Doctors

Responsiveness of Hospital Staff

Pain Management

Communication about Medicines

Discharge Information

Cleanliness and Quietness of Hospital Environment

Hospital Rating

What do the Survey Results Mean?

The survey and its results are extremely important, as it serves as the voice of the patient and gives a view into the patient's perception of the care that is provided. The survey results are publicly reported on the internet for all to see; therefore results directly impact a healthcare organization's reputation. Additionally, the government provides reimbursement based on results - so, excellent survey performance keeps the hospital financially strong.

New to LOGIC!

Now available in LOGIC - a deeper look into healthcare organizations' HCAHPS scores and how they compare to state and national averages. This information is categorized based on an approved set of measures and dimensions grouped into specific quality domains. To learn more on HCAHPS additions to the LOGIC market intelligence platform, click here.

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HCAHPS (the Hospital Consumer Assessment of Healthcare Providers and Systems) is a patient satisfaction survey required by CMS (the Centers for Medicare and Medicaid Services) for all hospitals in the United States.

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Big data is being increasingly applied to improve patient experience and outcome globally. Patient experience and outcome have been largely neglected so far mainly because hospitals believed that it needed to connect unemotionally and objectively with its patients and treat ailments. But the hospitals are realizing that patient treatment goes beyond physical ailments. It is about how the patient emotionally feels about several things such as behavior, facial expression, approachability, cleanliness, billing experience and empathy experienced in the hospital. Such factors constitute patient experience and hospitals are trying to quantify patient experience in the form of big data. Several use cases testify to the effectiveness of big data in improving patient experience and outcome.
What does patient experience and outcome mean?
A patient's experience with a hospital is not only connected to the quality of pure medical treatment but also with the overall emotional experience that includes, but may not be limited to ease of appointments, approachability and friendliness of staff, timeliness, empathy and cleanliness. Patients can award scores based on his or her overall emotional experience with the hospital. Big data technology is able to capture the emotional experience in the form of data. The image below shows that the broad range of emotions and experiences a patient can have is captured in the form of data in various mediums such as social media sites.

The data about patient experience can be sourced from different sources, as shown in the image below.

Given below are five powerful ways big data is being used to improve the patient experience and outcome.
The Cleveland Clinic way
The Cleveland Clinic significantly improved its patient experience with the help of analytics. Back in 2009, the clinic had not been receiving high scores on patient satisfaction index. Needless to state, that had a lot of implications. So, the CEO Dr. Cosgrove decided to dramatically improve patient services by relying on analytics. To turn the vision into reality, the clinic hired Dr. James Merlino as the Chief Experience Officer. Dr. Merlino hired a third-party agency to conduct a quantitative and qualitative study on what the patients expected from the clinic. The findings from the analytics were different from what the clinic thought the patients expected from the clinic. The patients, the analytics revealed, expected respect, clear and consistent communication and happy hospital staff. These expectations were connected with the emotional state of the patients. Patients wanted display of concern and empathy from hospital staff. It is significant that the study by the outside firm could quantitatively capture patients' feelings and deliver them as analytics.
Gaining insights from big data
Gaining insights is the first important step towards improving patient experience and there are a lot of ways to do that. First of all, data needs to be accessed from the different sources, as shown in the images above. After that, the data can be analyzed to find out patient feelings towards hospitals. For example, social media and website discussions could reveal that patients tend to feel a lot of anger at the billing inefficiencies of a particular hospital. Advanced analytics could quantify these ranges of emotions. Analytics engines could search websites such as Twitter to find out trending topics on healthcare and analyze the content. It is important to identify the most important issues in the minds of the patients - it could be availability of parking spaces, lack of clarity in communication, unclean bathrooms and even chaotic billing counters.
Identify trending or hot topics
The idea is to identify the trending topics and assign ratings to them to signify the seriousness or importance. For example, positive feedback could be awarded green colors and negative feedback could be given red colors. Advanced analytics is capable of generating reliable ratings from such feedback. Such feedback could also provide valuable materials for the hospitals to create Key Performance Indicators (KPI). The advantage with this approach of gathering and analyzing data is that it happens relatively quickly, when compared to the traditional method of surveys.
Creating action plans and goals
After identifying the trending or hot topics, the next step is to identify a set of variables that are playing a role in patient dissatisfaction. Variables are a set of parameters with assigned values that obviously, change over time. In this context, examples of variables could be billing errors, waiting time at the investigation departments, lack of process, poor attitudes towards patients and difficulty in taking appointments. After the variables are identified, the hospital can decide on what constitutes an acceptable change in variable values. For example, the hospital could target to limit billing errors to 1% of the total number of bills generated in a month. Data science is also capable of reasonably estimating the impact of the change in variable values on patient experience.
Reducing readmissions
According to Paul Muller, Chief Software Evangelist at HP, hospital admissions in the United States constitute about 30% of the total annual healthcare cost and 20% of all hospital admissions happen within 30 days of discharge. Muller observed, "In other words, we're potentially letting people go without having completely resolved their issues. Better utilizing big-data technology can have a very real impact, for example, on the healthcare outcomes of your loved ones." Obviously, reducing hospital readmissions could significantly improve patient outcome. But how could big data potentially help reduce readmissions? The key lies in accessing and analyzing medical and health data of the patients and developing plans accordingly. If a patient is readmitted within 30 days, something has most probably gone wrong with the post-discharge care. So, accurate analysis needs to be done on the possible risks, actions, emergency situations, medications, history of illnesses and so on.
Reducing avoidable expenses
According to Muller, medical errors are one of the biggest contributors to the avoidable medical expenses in the United States which could be as high as 17.6% of the GDP. Medical errors such as a sponge left in the stomach after operation or an overdose leading to infection could drive up the cost hospitals incur and the insurance expenses. Inefficiencies in the process also indirectly contribute to higher costs. This goes without saying that inefficiencies and errors can contribute substantially to patient dissatisfaction and reduce patient outcome scores. Big data analytics, combined with the right technology, can expose the problems in an objective manner. Deaths due to errors and negligence can drive patients away from a hospital. Right analysis can control the churn.
Summary
Patient experience and outcome have been neglected components of treatment for a long time and it is finally getting the attention it deserves. Big data most probably is the best way to address the issue. However, hospitals also need to consider the perspective of too much reliance on data. Statistics, at its best, can tell only a part of the story. The challenge is to getting its act together, by putting the action plan generated from the data in practice. There is another perspective also: can the investment in big data analytics also drive up hospital bills? That can be another area of patient experience. It will be interesting to see how hospitals play the balancing act.

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How big data is used to improve patient experience/patient outcomes?

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Big data is being increasingly applied to improve patient experience and outcome globally. Patient experience and outcome have been largely neglected so far mainly because hospitals believed that it needed to connect unemotionally and objectively with its patients and treat ailments. But the hospitals are realizing that patient treatment goes beyond physical ailments.

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2 Why does your hospital participate in NDNQI

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Doc Vader Vs. Patient Satisfaction Scores | ZDoggMD.com

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Patient Experience: Meeting Patient Needs and Transforming the Care Experience

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For those working in the nursing profession, decision making for caregiving is constantly required in real time in the clinical environment. Therefore, achieving a correct understanding of the decision-making process, as well as a high level of quality in clinical decision making, is crucial to minimize risk and error in the press of real time for the purpose of patient safety.

Decision Making

Theories and models of decision making have been presented and studied over decades. Paley et al and Bjørk and Hamilton categorized decision making as having two kinds of validity: rational systematic-positivist (decision making is analytical and logical) and phenomenological (interpretive, with a more intuitive approach). Concept attainment theory was discussed by Aitken as a rationalist way to make decisions. Aitken presented the concept attainment theory as a four-stage process, from generating attributes to developing hypotheses. This type of step-by-step information process is considered a linear process. It is arguable whether a decision-making process is linear. Social judgment theory is another of the important theories in clinical decision making. It explains the way in which the decision maker places values on the information. Consequently, information that is applied in different circumstances can result in a different judgment. Probability theory, also known as Bayesian theory, is a statistical model that is used to calculate the probability of an event in order to make a decision with a normative approach. Decision trees (a functional strategy for complicated situations) demonstrate the utilities and outcomes of each option.

In addition to the above rationally based theories, intuition plays an important role in decision making. However, "intuition has seldom been granted legitimacy as a sound approach to clinical judgment." Benner and Tanner emphasize the importance of intuition, which distinguishes expert judgment from inexpert, mechanical judgment. It is experts who can complete a clinical picture with efficiency and validity. The development of dual process theory effectively combines the reality and relative virtues of both intuitive and rational theory. There are two systems in dual process theory: System 1 is described as fast, holistic, and unconscious, whereas System 2 is slow, analytic, and conscious reasoning. Although System 1 and System 2 cooperate with each other, they do not appear necessarily to be operating at the same time. Finally, it is worth noting a seven-stage theory of decision making ( Table 1) suggests that certain stages in the decision-making process are often overlooked, including the recognition and formulation of the problem, action, and feedback.

Data Mining

The term "data mining" encompasses understanding and interpreting the data by computational techniques from statistics, machine learning, and pattern recognition, in order to predict other variables or identify relationships within the information. According to Finlay,(p2) data mining is commonly used to "identify relationships in data that give an insight into individual preferences," especially "what someone is likely to do in a given scenario." As far back as 1970, decision theory, combined with probabilistic criteria, was implemented to diagnose renal disease. Several approaches were introduced in the 1990s to support the diagnosis of lymph node disease. Probability systems with decision theory were developed within artificial intelligence, based on approaches for making clinical decisions. Several models apply to the process of data mining. Table 2 presents a summary of these models.

The 20th century was seen as the era of technology. The knowledge explosion continues in medical science and the clinical field. However, according to Yildirim et al, diagnosis of many diseases involves a substantial degree of uncertainty. Pattern recognition and generating provisional hypotheses based on patient symptoms, medical history, physical examination, and some tests are used by physicians for diagnosis. However, "clinical decision making is prone to error," especially in a complicated scenario with a great deal of information. Therefore, data mining has been developed as one way to minimize errors in decision making. Data mining is an effective method for extracting valuable knowledge from data. The models that are used in data mining are designed to resemble clinical decision-making strategies. Accordingly, a few data-mining methods are already being used to improve the process of decision making in various fields within the clinical area. Several applications and types of software have been developed to support clinical decision making, for example, a software application for detecting septic shock. The goal of data mining in clinical decision making is to recognize the pattern and relationships in attributes of the clinical setting and to estimate the outcome, to support clinicians when making decisions. Based on the above considerations, the aim of this literature review is to survey the diverse data-mining strategies used in real-time clinical decision making. A search strategy for the literature review is presented in the following sections. The article first demonstrates the strategies of data mining involved in the clinical field and then discusses the advantages and disadvantages of data mining in clinical decision making.

METHODS

A literature review was undertaken to survey the strategies used in data mining in clinical decision making. An integrated review was conducted with four databases: CINAHL, Cochrane Library, PubMed, and MEDLINE. Database searches of titles, abstracts, and key words were performed, using the following search terms: data mining , clinical decision making , and decision making, to expand the sensitivity and specificity of the results and ensure that all potentially applicable articles were included. The findings were limited to English-language publications with full text available; the chosen time period was January 2004 to November 2014. The review included articles connecting data mining with clinical environment, which influences the process of decision making. Articles on coding or informatics data-mining technologies were excluded.

Search Outcome

A total of 323 articles were found in the results from the four databases. Using the exclusion criteria, 254 articles were identified, with duplicates removed. The titles and abstracts were assessed, and 44 articles were recognized as relevant to data mining and decision making in general. After a screening of the full texts, 21 articles were eventually selected as relevant to clinical decision making.

Seven of the selected articles discussed data-mining models for the purpose of diagnosis. Of these seven articles, two were related to radiological imaging interpretations, and one related to nursing diagnosis. Four of these articles recommended the identification of an appropriate treatment for the patient. Some articles focused on advancing data-mining strategies, whereas others concentrated on improvements in dealing with adverse drug events. Three of the articles examined the prediction of a certain type of patient by data-mining models ( Table 3).

Data-mining theories were considered in all the articles. Among all of the theories, six used decision trees, and three used artificial neural networks. A cluster model was applied in two of the articles. A Bayesian network was identified in two of the articles.

The decision tree is an approach that classifies samples and has a flowchart-like structure. It is for predicting or presenting the value of objects in different categories by using classification algorisms.

Worachartcheewan et al identified a metabolic syndrome by using a decision tree model. The construction of the decision tree could define the metabolic syndrome by categorized attributes, with accuracy exceeding 99.8%. Through the decision tree strategy, three combinations of variables (triglyceride + blood pressure, fasting plasma glucose + blood pressure, and triglyceride + blood pressure + fasting plasma glucose) were extracted as the most important evidence for classifying metabolic syndrome.

Takada et al used decision trees to predict axillary lymph node metastases in breast cancer patients, in order to determine future treatment. A large dataset of breast cancer patients was provided. Although the model overestimated the proportion of patients at risk of axillary lymph node metastasis, it was accurate with respect to patients with lower risk of metastases.

Finlay et al illustrated the use of an artificial neural network. An artificial neural network appears as a two-stage process. In the first stage, each attribute is calculated into values and generates a "neuron." The interconnection pattern between the different layers of neurons will compute the weights of the interconnections. The learning process will convert an output produced by neurons in the second stage. Figure 1 shows an artificial neural network model, which is inspired by the action of the human brain.

In a study by Lu et al, the patient was classified as "tear" or "no tear" in the rotator cuff. The orthopedists made a diagnosis of a rotator cuff tear based on a physical examination. This had a high false-positive rate and could result in unnecessary imaging tests, such as magnetic resonance imaging (MRI), which incurs considerable cost. However, this neural network combined clinical examination with multiple personal characteristics (such as age, gender) and symptom history (such as pain index) to make a diagnosis and recommend a treatment plan. Doctors could use the results of this predictive data in making diagnostic decisions, especially if the pretest of a rotator cuff tear was intermediate. Tam et al presented the data-mining strategy of artificial neural networks with respect to osteoarthritic knees. Neural network training was conducted with input attributes, and the estimated outcome was then compared with the real outcome. Consequently, adjustments were made automatically. In the article, three treatments were chosen: transcutaneous electrical nerve stimulation, exercise, and transcutaneous electrical nerve stimulation with exercise. The authors applied the artificial neural network programming techniques with limited attributes to predict the appropriate treatment protocol. Finally, a suggestion would be made by the program ( Figure 2).

A Bayesian network is associated with probability distribution. For example, from the probability distribution, the fever and PO ₂/FIO ₂ ratio for a mechanically ventilated patient with pneumonia can be found with a pattern. Through these individual variables, the network will express this probability of an ICU patient with pneumonia. An example of another model-the cluster model-was given by Almasalha et al. Similar attributes were grouped, and historic data were gathered to search for patterns. The pattern generated a nursing diagnosis with nursing interventions suggested ( Figure 3).

According to Baceanu et al, the Expert Explorer is a Web-based data visualization tool that can generate reports, update datasets, import rules, and load rules. Experts view the interpretation of the data and give their advice. The data-mining software then learns the rules, which could help to alert clinical staff to adverse drug events. Research by Bowles et al compared the decisions made by a human expert and a data-mining expert model, which judged a patient according to six factors. The data-mining expert model produced 87.6% accuracy. In a review of the literature, Wagholikar et al described an EXPERT model, which is a rule-based model, built with hypotheses, findings, or observations; decision rules were set for the logical relationships between variables and the database. The system tried to give interactive advice for the users. It was used within rheumatology, ophthalmology, and endocrinology. Like heuristics in clinical decision making, heuristic algorithms are the fastest strategies for data mining, but may not be the best method for decision making. Wagholikar et al found the first heuristic system was developed in 1980s by Kulilowski, who interpreted the disease process with a descriptive model and developed consultation systems for neuro-ophthalmology, eye infections, rheumatology, and pathology. A knowledge discovery database (KDD) could be functional in various fields, such as the interpretation of a radiology examination, determination of uncertainty, and clinical care decision making. One of the clinical decision-making scenarios involving KDD illustrated by Reiner was the interpretation of computed tomography and MRI of an emergency patient, which can help clinicians to distinguish strokes.

One of the advantages of using data mining is to increase both computational and diagnostic efficiency. According to Orthuber and Sommer, the time for calculating 1 million vectors within double accuracy was between 0.20 and 0.21 seconds. Batal and Hauskrecht developed a model that was able to minimize the predictive rules and attributes and to lessen time for decision making. It is evident that data-mining technology can manage a great amount of data efficiently. Wagholikar et al mention, interestingly, that the progress of data mining for complex problems was better than for simpler problems. In addition, using data-mining strategies in clinical decision making can be accurate, especially when forecasting or diagnosing. Lu et al indicate that compared with physical examination, with 40% to 98% accuracy, the data-mining tool can detect a rotator cuff tear with 83% to 95% accuracy. Morrison et al state that the accuracy of the probability of malignancy for pulmonary disease decreased unnecessary computed tomography or pulmonary angiography. Several comparative studies have also highlighted the accuracy of data-mining models such as decision trees. Accuracy can eventually improve patient safety and reduce medical errors. However, Takada et al point out that, compared with the diagnostic performance of human experts, data-mining strategies are not accurate enough.

Although data mining can be useful and efficient, it has a few limitations. First, a huge database is required to build up a data-mining model or to define the patterns. For example, a tool for determining treatments for breast cancer patients used the database built by gathering data of 474 breast cancer patients over 5 years. Nevertheless, the use of a data-mining model might be restricted to a specific disease under a certain condition, which means that the tools can only help certain groups of patients with limited conditions, and some of the data-mining strategies might not lead to an interpretation if there were a missing attribute. Moreover, even though the pattern between the decision and the attribute is found, explanations are seldom provided. A few factors were relatively important for the decision, according to Lu et al. However, it was not clear how the correlations between the diagnosis and these factors were established. This can create an uncertain environment for clinicians to make a judgment with data-mining strategies.

DISCUSSION

Data-mining theories and models are similar to the clinical decision-making model; for instance, decision trees occur in both fields, and neural networks with concept attainment theory and heuristics are used in both areas. In decision tree theory, both data mining and clinical decision making use the branches in a decision tree to classify the options of various decisions. Neural networks are similar to concept attainment theory, as they are both linear processes with step-by-step approaches. They generate all attributes to form a hypothesis and then evaluate hypothesis to generate a final score. In data-mining heuristic algorithms, they are the fastest strategies, but may not provide the best decision. Similarly, heuristics in decision-making theory represent an immediate decision that may not be ideal. Nevertheless, one of the most beneficial issues of data mining, compared with clinical decision making, is the feedback system, emphasized in every model. In contrast, clinicians seldom receive any feedback for the judgments they make.

Data-mining theories are a more rational system of decision making. Although data mining is powerful at directing complex situations, it is limited by current technology. Compared with humans, who have, in general, a "limited channel capacity," an expert nurse may be able to decide an appropriate plan of care by intuition with only a few attributes, whereas data mining is restricted by the database and the conditions of the disease. As nurses, we use our decision-making theories to take care of individual patients in order to provide personal care. On the other hand, data-mining strategies are designed for a group of people. It is arguable whether data mining can come to a decision by itself for individuals.

The results of data mining will affect a clinician's decision making. However, current studies do not mention the correlation in decision making between human beings and information technology. Future studies are required to examine the effect of embedding data-mining models for clinicians.

CONCLUSION

Data mining is an information technology with an innovative effect on the way that people live, communicate, and learn. The technology aims to assist clinicians in clinical decision making and promote patient safety. Several data-mining models have been embedded in the clinical environment to improve decision making and patient safety. This review surveyed the data-mining strategies in clinical decision making and also assessed the disadvantages and advantages of using data mining in clinical decision making, through a literature review of 21 articles. Various aims of data mining were identified, and different data-mining models were introduced in the articles, including a decision tree model for presenting the value of objects in different classifications, a neural network model that gathered attributes and then performed a comparison of the outcomes, a Bayesian network that focused on probability between attributes, an expert model that had a high degree of accuracy, and a KDD that assisted with interpreting imaging. Data mining is efficient with high accuracy. On the other hand, lack of explanation, such as inadequate scientific evidence, is one of the disadvantages because of the problem of working with missing attributes and its limitation with certain conditions or diseases. Predictive data mining is becoming an essential instrument for researchers and clinical practitioners in medicine. Understanding the main issues underlying these methods and the application of agreed and standardized procedures are mandatory for their effective deployment and the proper dissemination of results.

References

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Using Data Mining Strategies in Clinical Decision Making: A ... : CIN: Computers, Informatics, Nursing

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these strategies in data mining in clinical decision making. A literature review was conducted, which identified 21 relevant articles. The article findings showed that multiple models of data mining were used in clinical decision making. Although data mining is efficient and accurate, the models are limited with respect to disease and condition....

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It Began in December, with CVS's proposed $69 billion buyout of insurer Aetna (aet). In January, three more corporate behemoths-Amazon (amzn), JPMorgan Chase (jpm), and Berkshire Hathaway (brk-a)-said they were forming a joint venture aimed at reducing health care costs and improving outcomes for their combined 1 million or so employees. Then, in March, Cigna (ci) said it would buy pharmacy benefits manager Express Scripts for more than $50 billion.

What's driving this frenzy of health care-related dealmaking? On first glance you might think it's merely the pursuit of mass itself. Of "scale," as management types like to say. But in truth, there's a more powerful catalyst-one so gargantuan and infinitesimal at the same time that it sounds like the answer to a riddle. And that's data.

More specifically, it's your data: your individual biology, your health history and ever-fluctuating state of well-being, where you go, what you spend, how you sleep, what you put in your body and what comes out. The amount of data you slough off everyday-in lab tests, medical images, genetic profiles, liquid biopsies, electrocardiograms, to name just a few-is overwhelming by itself. Throw in the stuff from medical claims, clinical trials, prescriptions, academic research, and more, and the yield is something on the order of 750 quadrillion bytes every day-or some 30% of the world's data production. These massive storehouses of information have always been there. But now, thanks to a slew of novel technologies, sophisticated measuring devices, ubiquitous connectivity and the cloud, and yes, artificial intelligence, companies can harness and make sense of this data as never before. "It's not the data," says Eric Topol, director of the Scripps Translational Science Institute. "It's the analytics. Up until three-to-five years ago, all that data was just sitting there. Now it's being analyzed and interpreted. It's the most radical change happening in health care."

The quest to retrieve, analyze, and leverage that data has become the new gold rush. And a vanguard of tech titans-not to mention a bevy of hot startups-are on the hunt for it.

Alphabet (googl) life sciences arm Verily is aiming to create a "baseline" of human health by tracking all kinds of biometric information from 10,000 volunteers (and is rumored to have an interest in the health insurance business). Apple (aapl) just released an iPhone feature offering users in several big health systems instant access to their own medical record-an effort that joins its ongoing heart study with Stanford, testing if wearables can detect serious cardiac conditions.

Tapping this reservoir, say many, will ultimately improve patient health and decrease medical costs, which are projected to rise 5.3% in 2018 alone, according to the Centers for Medicare & Medicaid Services. That's a noble aspiration, certainly. But not lost on anyone is that it's sure to make for a potentially blockbuster business too. David Friend, managing director at BDO, points out that data-rich Facebook and Google make their money on advertising-a business worth $200 billion, he estimates. "Health care is 15 times bigger than that," he says. "We spend $3 trillion. In theory, if this is done right, you'll have 15 Facebooks and 15 Googles. That's what's up for grabs."

Which is why so many old-guard health care companies, from hospitals and insurers to benefits managers and drug and device makers-which together account for one-fifth of the economy-are hastily recombining and reinventing themselves. The realignment promises not only to drastically reshape the health care landscape for companies overall, but for you as well.

Optimists, like Accenture's chief technology and innovation officer Paul Daugherty, predict that the "information asymmetry" will soon favor patients whose ownership of their own biological data will give them new power.
Read more about this topic in How This Rural Hospital Is Outdoing Silicon Valley.
To see what the new balance of power will look like in the coming years-and what it looks like right now- Fortune interviewed more than three dozen executives at companies across the health care continuum, along with entrepreneurs, doctors, patients, and other experts. Here's how the big-data revolution is-and isn't-transforming medicine.
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The Data Pill: A New Paradigm for Patients

Right around his eighth birthday, Lindsay Amos noticed her son Jacoby seemed not quite himself. The usually active boy who played hockey and lacrosse was sluggish, and he seemed to be going to the bathroom a lot. When the boy's doctor took a reading of Jacoby's blood sugar, the family was told to rush to the ER. On the drive there, the eight-year-old drifted in and out of consciousness. Jacoby's blood sugar, Amos later learned, was 735 mg/dL, compared with a healthy range of 70-140 mg/dL. The boy was lucky not to have developed diabetic ketoacidosis, or DKA, a sometimes fatal complication in which, owing to prolonged elevated glucose levels, the blood becomes acidic, and organs begin to shut down.

The resolution to those terrifying events struck Amos, who lives in the Denver suburbs, as startlingly casual and vague. Her family received a crash course on Type 1 diabetes, and the life-threatening consequences of both dangerously high and low blood sugar. They were told to count Jacoby's carbohydrates and check his blood sugar throughout the day-a process that involves pricking a finger and using a diabetes test strip-and to record this all manually in a logbook.

The dance between stress, exercise, insulin, various kinds of food-and what impact all those factors and more have on blood sugar-is notoriously hard to master, and trying to keep Jacoby in a healthy range was both exhausting and frightening. She'd dutifully do the math, but it didn't seem to matter. Jacoby was on a roller coaster: His glucose level swinging unpredictably to alarming highs (when he'd feel fatigued) to dangerous lows (when he'd feel dizzy).

His life depended on this number. Amos wanted to have an idea of where it stood at all times and for the first few weeks after Jacoby's diagnosis, she did her best-testing her son's glucose levels 20 or so times a day, far exceeding the number of test strips their insurance provider covered.

This everyday trauma affected millions of American families each year; then, in 2015, a bit of smartphone technology took away some of the worry. A California company called Dexcom connected a continuous glucose monitor (a device that had been around for more than a decade) wirelessly to a smartphone (or smart watch), allowing the user to read, plot, and share blood sugar levels with anyone, at five-minute intervals, all day long-and sending an alert when patients were at risk.

While some experts believe such always-on devices can leave patients with too much information, Amos-who half-jokingly speaks of "stalking" her son's glucose levels-says it's a lifesaver. Jacoby is now just another normal third-grader, she says. Rather than leaving class to go to the nurse's office multiple times a day, he discreetly monitors his blood sugar on his Apple Watch. (Amos watches too, on her iPhone.) And instead of waking every few hours to have his finger pricked, he sleeps through the night.

But the device offers more than just peace of mind. The data it generates has actually helped Amos and Jacoby understand his diabetes and how to manage it. It lets them see what foods make his blood sugar soar and how best to time his insulin shots around complex carbs like pizza. No, his diabetes isn't cured. But his blood sugar is now predictable and rarely triggers an alarm. It's remarkable how unremarkable the technology now is in their lives. "It's no different than a seat belt or a bicycle helmet," she says.
Read more about this topic in How Big Pharma Is Using AI to Make Better Drugs.
This is but one way in which smartphones and connected devices are changing the relationship between patients and their health data-and enabling them to improve their health in the process.

Digital diabetes prevention and treatment platforms such as Virta and Omada Health connect users with support communities and health coaches-who can remotely monitor things like weight, blood sugar, diet, and medicine intake. Then there's Proteus Digital Health's ingestible sensor, which-with a technology worthy of an episode of Black Mirror -helps patients (and, if they want, their doctors and family members) keep tabs on whether or not they're taking their meds. By pinging an app every time a pill hits stomach acid, the sensor gamifies the prescription process, as Michael Anderes, the chief innovation and digital officer at Froedtert and the Medical College of Wisconsin Health Network, puts it. Hepatitis C patients on expensive drugs like Gilead's Harvoni were 98.6% compliant in taking the medicine at the right time when using the sensors, in Froedtert's experience. That's not just critical for curing the disease, it's also a major money saver because an extra month of medication would cost tens of thousands of dollars. Which is why Froedtert (not its patients) foots the bill for the sensor.

Wearable health trackers like the popular Apple Watch or Android-based devices are now alerting their owners to everything from sleep apnea to hypertension to even serious cardiac arrhythmias. And increasingly, that self-awareness is drilling into our own genomes, helping people-if for now, imperfectly (and controversially)-gauge their risk of developing certain diseases, such as cancer and Alzheimer's. Consumers are turning to ever-cheaper, spit-and-send genetic test kits offered by companies like Color Genomics and 23andMe, to forewarn them of specific genetic susceptibilities-an awareness that, boosters say, can sometimes enable individuals to take preventive action that may mitigate those risks.

Out of the blue, such at-home gene tests have become consumer must-haves: On the most recent Black Friday, 23andMe's standard DNA test was one of Amazon's top five sellers-barely trailing Amazon's own Echo Dot and the "Instant Pot."

The selling point for all of these transformative technologies is a simple one: The consumer is in the driver's seat.

Some big insurers are even discovering that engaging patients with their data is a good way to improve outcomes and control costs. That's what Mark Bertolini is betting on. Bertolini, the CEO of Aetna, which is looking to combine forces with CVS (cvs), believes that consumers can be-and would actively want to be-data-sharing partners, if companies can demonstrate how consumers can benefit from that cooperation. "We have all these rules about protecting data," says Bertolini. "But if you turn it around and say to the customer, 'If we have this information about you, we can make this a lot more convenient for you,' he or she will give you the data. That's why social media works the way it does."

With that personalized data, then, the company can build a health plan in concert with those patients, says Bertolini: "We want to say to them, if we build a plan together, there are no copays and there are no authorizations because we built it together."
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Command Central: Better Decisions Through Data

Rodney M. was picking up a birthday present for his wife when the pain hit. The 52-year-old CEO of a communications firm clutched his chest and scrambled to a seat. He felt numb, paralyzed from the waist down. It felt like he'd been "hit by a truck."

Already a cancer survivor in remission for two years, Rodney had just suffered a whole new kind of medical nightmare-an "aortic dissection," caused by a tear in the critical artery that supplies the body's lifeblood.

He was lucky when the ambulance arrived quickly to take him to Howard County General Hospital, in Columbia, Md.-one in five aortic dissection patients dies before reaching a hospital. But his journey didn't end there: Within minutes, Rodney was being airlifted to Johns Hopkins in Baltimore-and after a complex, seven-hour surgery, survived.

He didn't know it at the time, but an artificial intelligence-powered data "command center" at Hopkins helped save his life. "Forty-one minutes. That's how long it took to get wheels up from the hospital," says Jim Scheulen, the chief administrative officer tasked with overseeing the medical center's sprawling emergency medicine unit.

The command center pulls in information from more than a dozen data streams in real time, including patient health records, emergency dispatch service updates, lab results, and tabs on how many hospital beds are available at any given time. Then, through its human-trained algorithms, it makes split-second decisions on triaging patients and getting them where they need to go-prepping the surgical team ahead of time in cases like Rodney's.
Read more about this topic in Who Owns Your Biological Data?.
For the hospital, the financial benefits of all this data management are unmistakable. "At Hopkins, there's been about a 60% increase in the ability to accept complex cancer patients, an over 25% reduction in emergency room boarding [those waiting for an inpatient bed], and a 60% reduction in operating room holds with the command center since its launch in February 2016," says Jeff Terry, who oversees such projects for GE Healthcare, which built the Hopkins command center. Hopkins's Scheulen says the technology functionally expanded the hospital's capacity by 15 or 16 beds without the need to add, well, actual beds. This year, GE (ge) plans to announce 10 new command centers covering 30 different hospitals-which, over five years, Terry claims, should yield those medical centers a roughly 4 to 1 return on their investment.

Far less whizbang, but likely more transformative, are recent upgrades to last generation's big-data breakthrough: the electronic health record (EHR).

It's hard to find a medical technology more universally hated than the EHR. Doctors complain that it consumes too much of their time, that they don't work well with other medical records systems, and that they're still largely indecipherable to patients. The technology's role in physician burnout has been explored in no less than 593 scholarly articles and one rap video since 2015. But if the big-data mission has found a worthy calling, it is here-transforming electronic health records from a time suck to a viable research tool.

That's what happened at Lakeland Health, a not-for-profit community health system in southwest Michigan. Lakeland got an EHR system in 2012-but it might as well have been composed of paper and pen. Nurses there recorded vital signs of every patient on charts-and once back at their workstation, would manually reenter the stats into the hospital's electronic system, a transcription process that ate up 15 to 20 minutes and often resulted in errors. Then, in mid-2016, they switched to a new process that enabled data to be automatically uploaded from patient wristbands or entered by nurses at their bedside on handheld devices.

One change was obvious from the start: Nurses spent less time on data entry and more time tending to patients, says Arthur Bairagee, Lakeland's chief nursing informatics officer. But more radical-and surprising-was the drop in "code blues": the warnings that patients were in cardiac and respiratory arrest. They've dropped a mammoth 56% since the new system, developed by Anglo-Dutch health giant Philips, was introduced in June 2016. Why? In part, the A.I.-driven warning system built into the monitoring technology, which not only picks up on even subtle changes in vital signs, but also assigns patients risk scores that help nurses prioritize their attention.

"It's important to be honest and pragmatic about where we are now," says Roy Smythe, chief medical officer for strategy and innovation at Philips. Many of the new digital and data tools aren't so much about providing care, but rather about making that care more efficient, smarter, and precisely delivered.

Like so many experts Fortune interviewed-even those who are gung ho about digital health-Smythe cautions against overhyping the new med tech.

"We have overpromised and under-delivered," says Brennan Spiegel, a physician and director of health services research at the Cedars-Sinai health system. "I consider myself a techno-skeptical techno-philiac. But there are way too many people in the Silicon Valley echo chamber who have never touched hands on a patient and don't understand how hard digital health is." Spiegel, who is also a professor of medicine and public health at UCLA, points to high-profile failures he's personally experienced in the field-including a 2015 Cedars-Sinai project to connect patients, through wearables like Fitbit, Apple Watch, Withings, and others, to electronic health records, which flopped spectacularly. "We didn't give patients the optimal messaging, and we didn't invite them in the most compelling way." So potential participants had little personal rationale to connect and stay engaged with the program because it didn't present a tangible value proposition. "Digital health is not a computer science or an engineering science; it's a social science and a behavioral science."

Eric Topol, at Scripps, who's also a renowned cardiologist, sounds a similar cautionary note. "There's a tremendous amount of promise, but so much is unfulfilled," he says, owing to a variety of systemic roadblocks. Among the challenges, he says, are America's rigid and "long in the tooth" medical establishment-half of U.S. doctors are over 50-which is resistant to changing its ways "unless it's going to lead to higher compensation."

Fighting the "Failure Problem"

To hear the folks at Amgen (amgn) tell it, big data has upended the California biotech's drug development process and significantly reshaped its pipeline. That story begins in 2011, when R&D chief Sean Harper, started making trips to Iceland. He was trying to solve his company's-which is also the industry's-"failure problem," which is summed up by the fact that 90% of drug candidates fail to make it to market.

Discovering new medicines is a wildly expensive and inefficient endeavor. Companies will often invest billions of dollars and many years chasing a promising scientific hypothesis. Pharmaceutical scientists hope for a moment of chemical serendipity, despite often not fully understanding the complexity of the biological mechanisms they're targeting-or why something might fail in humans when it works so neatly in a mouse model.

For Harper, Iceland seemed to offer an unparalleled pool of health-related data. The collection of that data-the genetic sequences of 160,000 citizens, along with their medical and genealogical records-was made possible by the Icelandic government, and the storage and analysis of that data was overseen by deCode, a Reykjavík-based human genetics outfit that, since its founding in 1996, had struggled to stay afloat financially.

Despite its solvency issues, deCode had become a prolific publisher of genetic discovery. Its trove of data allowed the company to mine the population for genetic variants and connect those variants to clinical outcomes in diseases ranging from cancer to schizophrenia. As the cost of sequencing plummeted in sync with the rise of computer processing power, Harper saw an undervalued asset for drug discovery: Amgen bought the company in 2012 for $415 million.
Read more about this topic in Helping Doctors Win the Data War.
That purchase has utterly transformed Amgen's R&D process. Prior to the deCode acquisition, only 15% of Amgen candidate molecules had been validated against specific genetic targets. After the purchase, Amgen began evaluating all of its drug candidates against deCode's database. The review exposed some clear losers; in the case of 5% of its candidate molecules, there was evidence the agent wouldn't work. Managers killed those programs (including one highly anticipated drug aimed at coronary disorders that was about to head into human trials) and prioritized others where there was a clear genetic target for the drug. Amgen also green-lighted more than a dozen drugs for which it found confirmation in deCode's genetic data.

Today, three-quarters of Amgen's pipeline is based on genetic insights largely gleaned from the database, says Harper, and the company has more than earned its investment back.

While having genetic validation for a target is no guarantee of success-scientists still have to figure out how to drug the target safely and effectively, and meet myriad other biological challenges-it does offer a head start. Says Harper, "If you can increase your rate of return by 50%, that's enormous."

Regeneron, a biotech that was looking to partner with deCode around the same time Amgen bought it, has a similar strategy. But rather than buying up a genetic research outfit, it decided to build its own in the form of the Regeneron Genetics Center (RGC), an ambitious four-year old effort to sequence as many exomes (the protein-encoding part of the genome) as possible, pair them with medical records, and accelerate drug development.

Lots of people talk about the promise of using genetics in pharma R&D, says Jeff Reid, Regeneron's chief of genome bioinformatics, "but they don't have a vision for sample flow." Translation: They don't have the data. Reid joined Regeneron when he learned the company had partnered with Geisinger (see "Keystone Care"), a Pennsylvania-based health care system with which it planned to collect and sequence samples from 100,000 consenting patients who also have comprehensive medical records. "They wanted to take this data and actually improve the care of patients," says Aris Baras, who heads up the RGC.

The company has since partnered with more than 60 other sources including the UK Biobank, which has recruited 500,000 participants. Baras and Reid say the scale and diversity of its growing data set, along with the ability to make all of these discoveries in-house, are critical. The work has so far spawned 50 target biology programs.

Hidden Figures: The Untapped Value of Medical Records

A random assortment of health information doesn't mean much if it doesn't meet at least two critical criteria, says oncologist and former Duke professor Amy Abernethy: quality and context. "Anyone who doesn't understand the core aspects of practicing medicine can't understand how messy it is," says Abernethy, who four years ago became the chief medical officer at Flatiron Health, a startup backed by Google Ventures (GV).

Take cancer records-Flatiron's specialty-as an example. Many of the most useful nuggets in an oncology EHR (about half, in fact) may reside in doctors' notes that aren't structured into specific data fields. These are the sorts of observations that can't be neatly packaged into categories on a form.

"Historically, these electronic records are billing and collection tools, documentation we have to comply with to get paid," explains Jeffrey Patton, a physician and CEO of Tennessee Oncology, a community-based health system that treats the largest number of cancer patients in the state and is one of hundreds of community cancer centers that now uses Flatiron's system.

Flatiron's selling point, ironically, is humans. When it comes to this type of data, it seems, people can figure out things a purely computer-driven system might miss. The real challenge isn't to gather the data, but to "clean it up," says Abernethy. "And that's really hard without an understanding of context."

Flatiron now has data from 20% of active cancer patients in the U.S., "and it's extremely well structured," says Daniel O'Day, CEO of Roche Pharmaceuticals, a unit of Roche Holding AG, which snapped up Flatiron in a $1.9 billion deal announced in February. "What set Flatiron apart was that it was able to create regulatory grade, real-world data," O'Day tells Fortune -data that O'Day claims is so well curated, that it "could have theoretically replaced the 'control' arm" of one of Roche's own clinical trials for the cancer immunotherapy drug Tecentriq.

In theory, Flatiron's system could have a broader impact on clinical trials accrual-for generations, one of the most stubborn challenges in cancer drug research-making it easier, for example, to match up specific patients with appropriate drug studies.

That, indeed, is an area where IBM Watson has already found some success. In March, the Mayo Clinic reported that using Big Blue's advanced cognitive computing system increased enrollment in clinical trials for breast cancer by 80%. "Watson is able to give us faster, better matching of patients to potential clinical trials that our oncologists wouldn't have otherwise been able to see," Mayo Clinic CIO Christopher Ross told the trade publication MobiHealthNews.

Even organizations which previously only had distant relationships with patients-pharmacy benefit managers, for instance-are, because of the vast kingdoms of data they oversee, well positioned to draw insights that might improve population health and lower costs.

Consider Express Scripts, the Missouri-based PBM that just announced its sale to insurer Cigna in March. Express Scripts administers 1.4 billion prescriptions for 100 million Americans each year-and it knows when you don't take your meds. Such nonadherence costs between $100 billion and $300 billion a year, depending on which estimate one believes. That cost comes when patients suffer complications from not following the doctor's orders.

The company has identified 300 different factors that can help determine the likelihood that a patient will not fill a prescription says Express Scripts' chief data officer Tom Henry. They range from that basic demographic data (income level and zip code) to behavioral data (one's level of forgetfulness and tendency to procrastinate, gleaned from surveys the company does after patients fail to pick up their prescriptions) to less intuitive things like the genders of the prescriber and patient (men with a woman physician are more likely to not follow orders). The company uses the algorithm, which it says is validated and 94% accurate, to assign risk scores to patients and target them with varying modes of outreach-Henry says those efforts are "soft touch, nothing Orwellian." Nonetheless, Express Scripts claims this work has reduced nonadherence by 37% and saved its clients more than $180 million.

As with any revolution, many rush into action without considering weighty questions about what comes next and what unintended consequences may arise. In this social experiment, those questions cover everything from patient privacy to the ethical dilemma of warning someone about a risk they can't avoid. To many digital health evangelists, big data is the "magic bullet" we've been waiting for. The question is, where exactly that bullet strikes.
A version of this article appears in the April 2018 issue of Fortune with the headline "Big Data Meets Biology."

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Tech's Next Big Wave: Big Data Meets Biology

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It Began in December, with CVS's proposed $69 billion buyout of insurer Aetna (aet). In January, three more corporate behemoths-Amazon (amzn), JPMorgan Chase (jpm), and Berkshire Hathaway (brk-a)-said they were forming a joint venture aimed at reducing health care costs and improving outcomes for their combined 1 million or so employees.

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1.  While data about patients is important, what collected data do you feel should remain protected?

2.What is your take on HCACHPS and the NDNQI survey?