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Big love-for-big-data-the-remedy-for-healthcare-quality-improvements 6836135

  1. 1. Next reports Rep or January 2014 $99 Big Love for Big Data? The Remedy For Healthcare Quality Improvements Data analytics requires learning from the past. Then why do so many big data efforts fail in healthcare organizations? In this report, we apply sage principles of quality improvement for scalable healthcare data analytics that actually see results. By Richard Hoffman Presented with Report ID: S7700114
  2. 2. Previous Next CONTENTS reports 3 4 5 5 6 7 7 8 8 9 10 11 12 13 Author’s Bio Executive Summary The Remedy For Healthcare Quality Improvements Figure 1: Approach to Managing Big Data Analytics Initiative Figure 2: IT Project Plans Planning The Revolution Warehousing Data For Analytics Playing The Political Game Figure 3: Importance of Healthcare IT Initiatives Bad Data Kills Projects Figure 4: Planned IT Projects Figure 5: Big Data Analytics Vendors Big Data In Big Health Related Reports The Remedy for Healthcare Quality Improvements ABOUT US InformationWeek Reports’ analysts arm business technology decision-makers with real-world perspective based on qualitative and quantitative research, business and technology assessment and planning tools, and adoption best practices gleaned from experience. Our staff: Lorna Garey, content director; Heather Vallis, managing editor, research; Elizabeth Chodak, copy chief; Tara DeFilippo, associate art director; Find all of our reports at TABLE OF January 2014 2
  3. 3. Previous Next Table of Contents reports Richard Hoffman InformationWeek Reports Want More? Never Miss a Report! Follow The Remedy for Healthcare Quality Improvements Richard Hoffman is owner of Geomancy Consulting, an InformationWeek contributor and former technology editor for Network Computing. He has been coding, analyzing and building systems for more than three decades since starting his first computer consulting business at the age of 14. Since doing academic work in areas including artificial intelligence, computational linguistics and knowledge representation, Richard has typically been on the bleeding edge of IT, including building portable/mobile and pre-802.11 wireless systems for the American Red Cross national headquarters for use on site at large-scale national disaster relief jobs; deploying one of the first websites with real-time remote updates and audio reporting from the field (with IBM, CNN and the Red Cross); building Web-based, classroom and e-learning systems for Fairfax Country Public Schools; leading intranet/portal system architecture for the U.S. Department of Health and Human Services; and directing Internet strategy and Web operations for Dartmouth-Hitchcock Medical Center in New England, as well as dealing with the perniciously sticky issues of governance, policy and process. He is currently an IT strategist, technology analyst, systems architect and semiprofessional heretic, based in New England but roaming frequently in search of interesting problems. He can be reached at Follow Follow © 2014 InformationWeek, Reproduction Prohibited January 2014 3
  4. 4. Previous Next Table of Contents SUMMARY reports EXECUTIVE The Remedy for Healthcare Quality Improvements Healthcare data is nothing new, but yet, why do healthcare improvements from quantifiable data seem almost rare today? Healthcare administrators have a wealth of data accessible to them but aren’t sure how much of that data is usable or even correct. With budget cuts and stretched staff, it’s a dicey proposition to ask a physician to take time to collect procedural data when she could be providing patient care. What’s a healthcare provider to do? The tantalizing promise of big data is that for the first time, with an abundance of data sources and efficient analytics tools, healthcare will finally have the information necessary to plan, achieve, and measure those quantifiable program improvements — to find success in meeting quality improvement goals that were important 10 or 20 years ago, and are absolutely essential now. Of course, too much of anything is a bad thing, and even with the best tools, you can’t turn bad data into good results. Big data and the increasing requirements around using that data can be a huge ally or your worst enemy. Your choices can help determine whether the flood of data will lift your organization’s boat — or sink it. Before you throw in the towel, we have a toolbox of systematic best practices to help you create a well-informed action plan for healthcare analytics. This report will guide you through the hairy politics involved with getting senior leadership and staff buy-in for data analytics initiatives and how to establish a governing body to take ownership of those big, audacious goals. We’ll call back to the gold-standard lessons of total quality management and continuous quality improvement that were set in place decades ago and apply them to today’s problems. What’s more, we’ll show you how to turn healthcare deadlines like ICD-10 into leverage to make the analytic improvements you need. Applying analytics to the world of healthcare data isn’t a “set it and forget it” proposition. You need a lasting solution that can react to changes in the status quo and also predict new and impending disruptive technology. By applying good data against gold-standard improvement strategies, you can enjoy a wealth of improvements for years to come — and avoid costly analytic disasters. January 2014 4
  5. 5. Previous Next Table of Contents reports The Remedy for Healthcare Quality Improvements The Remedy For Healthcare Quality Improvements The push to accumulate data continues relentlessly. With access to ever-increasing amounts of raw data and an array of new tools to process and analyze that data, healthcare program improvements — whether measured in specific outcomes or broader measures such as population health — seem like they should be within our reach. So why are actual quantifiable results the exception rather than the norm? “Data, data, everywhere, and not a program improvement in sight.” This astute observation from W.R. Cozens, former director of a residential treatment facility in Honolulu, referred to efforts in continuous quality improvement (CQI) and total quality management (TQM) for healthcare in 2000, but it couldn’t be more relevant today. It seems that everyone is playing catch-up against requirements that grow closer and tighter every day. In the rush to move forward, sometimes the basics get left behind. With the rise in data quantity and the Figure 1 Approach to Managing Big Data Analytics Initiative What approach is your organization taking, or planning to take, to manage your big data analytics initiative? Using a data analytics vendor Using a data analytics vendor in addition to our own resources 17% 31% 52% Using our own data warehouse and analysts Base: 175 healthcare provider respondents who are piloting, will complete or have already completed a big data analytics initiative Data: InformationWeek 2013 Healthcare IT Priorities Survey of 451 business technology professionals, January 2013 capacity repositories and data analysis tools to handle them, data-driven design fundamentals still apply. What are you measuring? Why are you measuring it? Who is responsible for all of this? Sometimes you have to look to the past to R6430413/5 move forward. Consider the lessons taught by classic quality leaders such as W. Edwards Deming and Sister Mary Jean Ryan that form the foundation of a useful, successful data-driven healthcare system. Sure, it might feel like TQM and CQI are so January 2014 5
  6. 6. Next reports The Remedy for Healthcare Quality Improvements 26% 31% 25% 11% 9% Predictive analysis (clinical) 12% 11% 6% 21% 10% 9% 15% Big data analytics 9% 15% Personalized medicine 14% 8% 6% 7% 8% 8% 7% 13% 12% 18% Chronic disease management No plans 24% 21% 24% 10% 6% 5% 8% 4% 3%2%3% 12% Robotics for patient treatment or care Evaluating 23% 21% 27% 55% 19% 8% 6% 11% 11% 22% Business intelligence tools for analyzing medical data 21% 31% 24% Telemedicine or telehealth 8% 6% 7% 6% 18% 9% 4% 18% 13% 24% Patient Web access to personal health record Currently piloting 23% 16% 16% 17% 8% 8% 10% 31% Clinical decision support 33% Storage upgrade 17% 11% 12% 9% 16% 37% Network upgrade 15% 14% 8% 3% 12% 7% 4% 16% 9% 7% 4% 11% 38% Patient management system 45% Computerized physician order entry 13% 15% 15% 13% 10% 6% 4% 8% 14% 6% 6% 5% 3% 10% 46% Picture archiving communication system 48% E-prescribing EMR or EHR 56% 13% 9% 8% 9% 5% 5% 8% 24% 12% 7% 4% 8% 8% 1990s. But the lessons learned Figure 2 — and not learned — through IT Project Plans What are your organization’s plans for the following IT projects over the next 24 months? TQM/CQI processes from 20 years ago are applicable and viAlready completed Will complete within 6 months Will complete within 12 months Will complete within 24 months tal today. These processes require healthcare leaders to look systemically at people and processes, using facts (data) and utilizing continuous evaluation in order to improve quality. The key in turning a flood of information into actual usable, verifiable program improvements is all about the basics of quality improvement. Even further back, core principles Deming pioneered in the 1950s — taking a systems approach, trying to locate the sources of variation in outcomes, and separating out controllable from uncontrollable variation — are arguably more crucial now than ever. Our own data shows this is a Base: 363 respondents working at a doctors’ practice, hospital, healthcare center or other healthcare provider high priority for most: According 20% Table of Contents Fraud identification systems Previous R6430413/3 Data: InformationWeek 2013 Healthcare IT Priorities Survey of 451 business technology professionals, January 2013 January 2014 6
  7. 7. Previous Next Table of Contents You’re Not as Agile as You Think Every shop says it uses an Agile development process, but almost none really does. Here are the three reasons why, and steps to get back on track. Download reports to respondents to InformationWeek’s 2013 Healthcare IT Priorities Survey, about a third (31%) of organizations had no plans to engage in a big data analytics project in the next 24 months, while 21% were evaluating such plans. About 33% of organizations were already engaged in big data analytics projects, and 15% had already completed their analytics project. Before you dive into implementing data analytics, you’ll need to take a hard look at your objectives. The toolbox is the same whether for clinical decision support, quality and outcome measures, population health, or other data-driven program improvements. The foundation of a successful big data initiative applies regardless of what data repository (or repositories) you have, and which data analysis tools are used. Planning The Revolution First and foremost, determine who is ultimately responsible for data flow and quality. Governance for data quality and program improvement should reflect the overall organizational structure and touch every part of the The Remedy for Healthcare Quality Improvements DATA REPOSITORY Warehousing Data For Analytics D eploying a central, dedicated data warehouse for use by analytic systems will yield much better results and allow for significantly greater flexibility and capacity than trying to analyze a distributed set of separate, independent data silos. The form, structure, and type of repository will reflect the needs of the organization. For instance, translational research will dictate a significantly different warehouse than a system whose primary goal is process and effi- healthcare organization. It can’t be done piecemeal. It truly is an “all in” endeavor. Formal organizational responsibility can be held within one department or group — or several. That decision will probably be political as well as structural. However the formal structure plays out, functionally there must be a level where “the buck stops here.” A specific person or group makes sure that the right ciency improvements. Both may make use of electronic medical records, claims, pharmacy, labs, and billing information, but a translational system will also tie in clinical trials and other research-related data sources. A repository used for population health research will need to pull in additional sources of information beyond those needed for the simple outcomes research and process improvement that a single facility might require. —Richard Hoffman data is collected at the necessary level of quality and ensures that data gets analyzed and evaluated in meaningful ways, and that competing priorities in data collection and analysis can be balanced and resolved. There are never enough resources to do everything that everyone believes is a priority. Meaningful Use is important, and so is improving quality and outcomes, reducing cost of January 2014 7
  8. 8. Next reports Playing The Political Game Next, you’ll need to put the right metrics in place to evaluate that data. Involve the stakeholders and identify the specific key metrics that will accomplish the “what” once the “why” Figure 3 Importance of Healthcare IT Initiatives How high a priority will the deployment or expansion of IT systems for the following initiatives be for your organization over the next 12 months? Please use a scale of 1 to 5, where 1 is "not a priority" and 5 is “top priority.” Note: Mean average ratings Base: 363 respondents in January 2013 and 337 respondents in January 2012 working at a doctors’ practice, hospital, healthcare center or other healthcare provider Data: InformationWeek Healthcare IT Priorities Survey of business technology professionals has been identified. Hands-on staffers need to R be fully involved in this process to make certain Personalize medical care 3.7 3.6 3.8 3.6 Share data with more providers and payers (e.g., on health information exchanges) Improve collaboration among clinicians 3.8 3.8 3.8 3.7 Improve collaboration between clinicians and patients Increase clinician efficiency 4.1 4.1 4.2 4.2 Improve care (decision support tools, e-prescriptions, etc.) 4.3 Reduce costs 4.1 4.3 4.3 4.4 4.4 2013 Manage digital patient data (electronic medical records or electronic health records) 2012 Meet regulatory requirements services, improving efficiency, and so on. The right person needs to ask, “With limited resources, where and how should we focus and coordinate our efforts, and are there ways to solve two, three, or more problems with one well-placed process?” That team, committee, or other group has to be empowered to make the necessary decisions and provide concrete guidance. As a core part of this work, both initial and regular periodic strategic analyses ensure that efforts and priorities in analytics — and the anticipated benefits of that analysis — all align with organizational mission, vision, and goals. In other words, the question of “what” data we collect and analyze must go back to the fundamental question of “why” we are doing all of this work in the first place. C-suite buy-in and involvement are essential at this level. The Remedy for Healthcare Quality Improvements Top priority 5 Table of Contents 1 Not a priority Previous R6430413/2 that data obtained is valid and useful. This gets to the point of “how” the data will January 2014 8
  9. 9. Previous Next Table of Contents reports be collected, and often “when.” Staff members are usually very busy, so mandating extra time for collecting data is a tricky topic. Physicians and other direct care providers are already impatient and annoyed by the time requirements of electronic health records systems. If the staff has to make a decision between providing good patient care and creating clean, usable data, patient care will (and should) prevail. The success of your implementation will hinge upon doing everything necessary to avoid making the staff decide beAccording to our 2013 Healthcare IT tween patient care and Priorities Survey, “meeting regulatory creating data. Best pracrequirements” was by far the highest tices involve collecting patient and care data priority for respondents working at a from the staff when healthcare provider. they aren’t actively providing hands-on care. The project leaders should also work closely with staff before, during, and after implementation to maximize the efficiency of the entire process. Be transparent that data quality will lead to systemic program improvements. The more The Remedy for Healthcare Quality Improvements that you communicate that the initiative has obvious benefits and outcome improvements, the more the staff will be inclined to cooperate. Additionally, stress the balance between the improvements that will come from the data collection while respecting the interest of making the fewest inconveniences upon the staff as possible. If the staff doesn’t want to collect the data or doesn’t have any real buy-in about the why and the what, not to mention the how and the when, you can make a pretty sure bet that the resulting data quality will be low and/or inconsistent. Transparency and internal communication can make or break the entire initiative. Behind the scenes, actively involve your stakeholders by providing a regular assessment of how the data was used in actual program improvements. You can use that opportunity to detail whether any ongoing changes in priorities or processes are necessary. While it seems like a given, this technique prevents this entire effort from being an expensive waste of time and effort. Communicating the results, positive or not, to all staff also serves to increase confidence and improves organizational morale. Successes build confidence. Resist the urge to downplay setbacks — frank discussion of failures makes it clear your data analytics has the highest level of attention and oversight. A quality and program improvement initiative that is an opaque “black box” will likely ultimately fail, no matter how well intended. The worst result is that staffers believe — either correctly or incorrectly — that their efforts were allowed to become a bottomless pit where resources go to die. Good projects often fail — and marginal ones can succeed — because of the amount of effort given to internal communication and marketing. This is true of any complex project, but particularly one that touches all aspects of the business and is intended to bring substantial and transformative quality improvements. Bad Data Kills Projects Bad data produces poor or meaningless results, no matter how much you massage it. Data quality is something that has to be baked in from the start. Unfortunately, not January 2014 9
  10. 10. Previous Next reports The Remedy for Healthcare Quality Improvements quality expertise as early in your processes as possible. You’ll avoid reengineering, retooling, or redesigning major systems at the very Figure 4 Planned IT Projects Which of the following IT projects are completed or will be completed within the next 24 months? Note: Multiple responses allowed; percentages reflect a response of “already completed,” “will complete in six months,” “will complete in 12 months” or “will complete in 24 months” Base: 363 respondents in January 2013 and 337 respondents in January 2012 working at a doctors’ practice, hospital, healthcare center or other healthcare provider Data: InformationWeek Healthcare IT Priorities Survey of business technology professionals NA Big data analytics 42% 43% 39% Predictive analysis (clinical) 44% 40% Fraud identification systems 45% 45% Telemedicine or telehealth 45% 38% Personalized medicine Chronic disease management 50% 52% 52% 52% Business intelligence tools for analyzing medical data Clinical decision support 60% 60% 63% 69% Picture archiving communication system 64% 55% Patient Web access to personal health record 65% 60% Patient management system Network upgrade 70% 71% Storage upgrade E-prescribing 75% 75% 75% 77% Computerized physician order entry EMR or EHR 69% 73% 2012 80% 84% 2013 least. You can also avoid the painful discovery that your data, and the conclusions drawn from that data, are invalid years from now. Following the path of bad data is worse than flying blind — it’s like flying while a totally fictional scene is projected into the cockpit. Decisions made on the basis of that data will bear little to no resemblance to what actually needs to happen, and results will be no better than random chance. Interoperability has been a major issue, and still is with some systems. Getting access to a propriety data source from your electronic medical records or other core systems for analysis can be a major pain. Luckily, while Meaningful Use Stage 2 means significant work for many healthcare organizations, the interoperability requirements put healthcare information system R6430413/4 vendors under the gun to better enable data exchange. That’s good 30% 36% many organizations have in-house expertise in achieving high data quality. It’s absolutely worth hiring or bringing in top-notch data Robotics for patient treatment or care Table of Contents January 2014 10
  11. 11. Previous Next Table of Contents Like This Report? Share it! Tweet Like Share reports news for healthcare data analytics as we get closer to Stage 2 deadlines. The increasingly constant pace of change can at times be made to work toward your goal of data-driven program improvement. For example, ICD-10 adoption is a major endeavor for many healthcare organizations, large and small. Healthcare administrators are under a lot of pressure and numerous headaches because of the associated deadlines. But that same ICD-10 pressure and associated action items can be harnessed to provide rationale for examining data collection across the board. If you need to do major organizational surgery anyway, you might as well take advantage of the opportunity to refine your processes while you’re in there. “Blame it on ICD-10” can be a successful strategy for change if there is significant internal resistance. Finally, delegate to a person or a group the responsibility to anticipate and keep on top of trends in data collection and analysis. Healthcare data analytics continues to evolve, not only in terms of data repositories and The Remedy for Healthcare Quality Improvements Figure 5 Big Data Analytics Vendors Which vendor(s) is your organization using, or planning to use, for big data analytics? Oracle 24% IBM 16% InterSystems 5% Humedica 4% Atigeo 1% Explorys 1% Other 16% We’re still evaluating vendors 42% Don’t know 13% Note: Multiple responses allowed Base: 84 healthcare provider respondents using or planning to use a data analytics vendor for their big data analysis initiative Data: InformationWeek 2013 Healthcare IT Priorities Survey of 451 business technology professionals, January 2013 analysis tools, but also potentially disruptive factors like always-on patient-facing mobile R6430413/6 data collection (“the iPhone effect”). Someone or some group in your organization should alJanuary 2014 11
  12. 12. Previous Next Table of Contents reports ways be looking over the horizon, trying to divine what will show up with the next sunrise. Ideally, a cross-disciplinary tiger team of medical staff, IT/technical staff, and data analysts will devote regular time to assessing new and potentially disruptive developments in data collection and analysis. For an example, one of the biggest areas of coming disruption is the idea of mobile data collection via privately owned data-enabled devices. With smartphone adoption at an all-time high and still growing, many patients have what is essentially an active telemetry device on their person at all times. Consumer-level healthcare data collection devices are already active on the consumer market, some at sub$100 price points. Consider activity and exercise monitors such as Fitbit and Jawbone UP, or other wellness-related devices such as sleep monitors. In early 2013, the FDA approved the first wireless Bluetooth-enabled blood glucose monitoring system, which can upload data automatically to an iPhone. A small flood of similar inexpensive wireless-enabled medical care devices is expected to hit The Remedy for Healthcare Quality Improvements the market in 2014. As time marches forward, the consumerization of healthcare will gain steam. There are obvious potential problems here: data quality, privacy and confidentiality issues, interoperability, and so on. No matter how you look at it, the potential for continuous monitoring of patients and real-time upload of data is clearly a game-changer, and it is coming — that future is almost here. Big Data In Big Health One thing is certain — the future will only bring more and more data to your doorstep. You need to effectively analyze and use all of that data with an aim to continuously improve your outcomes, processes, and operations. The better foundation you can set now, the better able you will be to handle the flood and turn it into a gold mine of useful information. Having peered into the near future, by looking even further back into the past, we may find some wisdom that can help lead us to success. More than 2,000 years ago Pythagoras shared sage advice in his Golden Verses: “Never allow sleep to close your eyelids after you go to bed until you have examined all your actions of the day by your reason. In what have I done wrong? What have I done? What have I omitted that I ought to have done? If in this examination you find that you have done wrong, reprove yourself severely for it — and if you have done any good, rejoice.” As we speed ahead toward the fast-approaching horizon of data-enabled program improvement, with solid proactive planning and due care, using the right tools and developing solid strategies for change, it can be hoped that we shall all have frequent cause to rejoice. January 2014 12
  13. 13. Previous Table of Contents MORE reports LIKE THIS The Remedy for Healthcare Quality Improvements Want More Like This? InformationWeek creates more than 150 reports like this each year, and they’re all free to registered users. We’ll help you sort through vendor claims, justify IT projects and implement new systems by providing analysis and advice from IT professionals. Right now on our site you’ll find: Research: 2014 Analytics, BI, and Information Management Survey: Today’s enterprises want less complexity and more user-friendly, visual dashboards. Forty-six percent of respondents say ease-of-use challenges with complex software is one of the biggest barriers. Similarly, data visualization tools rated as the most interesting leading-edge technology, with an average 3.5 rating on a 1-5 scale. The NSA and Big Data: What IT Can Learn: Ever heard of Accumulo? CIOs can leverage this and other tools Big Brother uses to analyze online activities. Here’s how. The Data That Matters: Business leaders will grow tired of the big data hype if we don’t deliver meaningful insights clearly, in near real time. One answer: visualization technology. Newsletter Want to stay current on all new InformationWeek Reports? Subscribe to our weekly newsletter and never miss a beat. PLUS: Find signature reports, such as the InformationWeek Salary Survey, InformationWeek 500 and the annual State of Security report; full issues; and much more. Subscribe January 2014 13