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Big Love for Big Data? The RemedyFor Healthcare Quality ImprovementsData 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

R e p o r t s . I n f o r m a t i o nWe e k . c om J a n u a r y 2 0 1 4 $ 9 9

Presented with

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CONT

ENTS

TABLE OF

3 Author’s Bio

4 Executive Summary

5 The Remedy For Healthcare Quality

Improvements

5 Figure 1: Approach to Managing Big Data

Analytics Initiative

6 Figure 2: IT Project Plans

7 Planning The Revolution

7 Warehousing Data For Analytics

8 Playing The Political Game

8 Figure 3: Importance of Healthcare IT

Initiatives

9 Bad Data Kills Projects

10 Figure 4: Planned IT Projects

11 Figure 5: Big Data Analytics Vendors

12 Big Data In Big Health

13 Related Reports

ABOUT US

InformationWeek Reports’ analysts arm business technology decision-makers with real-world perspective based on qualitativeand quantitative research, business and technology assessmentand planning tools, and adoption best practices gleaned from experience.

Our staff:Lorna Garey, content director; lorna.garey@ubm.comHeather Vallis,managing editor, research; heather.vallis@ubm.comElizabeth Chodak, copy chief; elizabeth.chodak@ubm.comTara DeFilippo, associate art director; tara.defilippo@ubm.com

Find all of our reports at reports.informationweek.com.

T h e R e m e d y f o r H e a l t h c a r e Q u a l i t y I m p r o v e m e n t s

January 2014 3

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T h e R e m e d y f o r H e a l t h c a r e Q u a l i t y I m p r o v e m e n t s

Richard Hoffman is owner of Geomancy Consulting, an InformationWeek con-tributor and former technology editor for Network Computing. He has been cod-ing, analyzing and building systems for more than three decades since startinghis first computer consulting business at the age of 14. Since doing academicwork in areas including artificial intelligence, computational linguistics andknowledge representation, Richard has typically been on the bleeding edge ofIT, including building portable/mobile and pre-802.11 wireless systems for theAmerican Red Cross national headquarters for use on site at large-scale nationaldisaster relief jobs; deploying one of the first websites with real-time remote up-dates and audio reporting from the field (with IBM, CNN and the Red Cross);building Web-based, classroom and e-learning systems for Fairfax Country PublicSchools; leading intranet/portal system architecture for the U.S. Department ofHealth and Human Services; and directing Internet strategy and Web operationsfor Dartmouth-Hitchcock Medical Center in New England, as well as dealing withthe perniciously sticky issues of governance, policy and process.

He is currently an IT strategist, technology analyst, systems architect and semi-professional heretic, based in New England but roaming frequently in search ofinteresting problems. He can be reached at Richard_Hoffman@me.com.

Richard HoffmanInformationWeek Reports

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Healthcare data is nothing new, but yet, why do healthcare improvements from quantifiabledata seem almost rare today? Healthcare administrators have a wealth of data accessible to thembut aren’t sure how much of that data is usable or even correct. With budget cuts and stretchedstaff, it’s a dicey proposition to ask a physician to take time to collect procedural data when shecould 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 sourcesand efficient analytics tools, healthcare will finally have the information necessary to plan,achieve, and measure those quantifiable program improvements — to find success in meetingquality improvement goals that were important 10 or 20 years ago, and are absolutely essentialnow. Of course, too much of anything is a bad thing, and even with the best tools, you can’t turnbad data into good results. Big data and the increasing requirements around using that data canbe a huge ally or your worst enemy. Your choices can help determine whether the flood of datawill 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 createa well-informed action plan for healthcare analytics. This report will guide you through the hairypolitics involved with getting senior leadership and staff buy-in for data analytics initiatives andhow to establish a governing body to take ownership of those big, audacious goals. We’ll call backto the gold-standard lessons of total quality management and continuous quality improvementthat were set in place decades ago and apply them to today’s problems. What’s more, we’ll showyou how to turn healthcare deadlines like ICD-10 into leverage to make the analytic improve-ments you need.

Applying analytics to the world of healthcare data isn’t a “set it and forget it” proposition. You needa lasting solution that can react to changes in the status quo and also predict new and impendingdisruptive technology. By applying good data against gold-standard improvement strategies, youcan enjoy a wealth of improvements for years to come — and avoid costly analytic disasters.

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EXECUTIVE

SUM

MAR

Y

Table of Contents

January 2014 5

The push to accumulate data continues re-lentlessly. With access to ever-increasingamounts of raw data and an array of new toolsto process and analyze that data, healthcareprogram improvements — whether meas-ured in specific outcomes or broader meas-ures such as population health — seem likethey should be within our reach. So why are actual quantifiable results the ex-

ception rather than the norm? “Data, data, everywhere, and not a program

improvement in sight.” This astute observa-tion from W.R. Cozens, former director of a res-idential treatment facility in Honolulu, referredto efforts in continuous quality improvement(CQI) and total quality management (TQM) forhealthcare in 2000, but it couldn’t be more rel-evant today. It seems that everyone is playingcatch-up against requirements that growcloser and tighter every day. In the rush tomove forward, sometimes the basics get leftbehind. With the rise in data quantity and the high-

capacity repositories and data analysis toolsto handle them, data-driven design funda-mentals still apply. What are you measuring?Why are you measuring it? Who is responsiblefor all of this? Sometimes you have to look to the past to

move forward. Consider the lessons taught byclassic quality leaders such as W. EdwardsDeming and Sister Mary Jean Ryan that formthe foundation of a useful, successful data-dri-ven healthcare system.Sure, it might feel like TQM and CQI are so

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The Remedy For Healthcare Quality Improvements

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What approach is your organization taking, or planning to take, to manage your big data analytics initiative?

31%

17%

52%

Approach to Managing Big Data Analytics Initiative

Base: 175 healthcare provider respondents who are piloting, will complete or have already completed a big data analytics initiativeData: InformationWeek 2013 Healthcare IT Priorities Survey of 451 business technology professionals, January 2013

R6430413/5

1Using a data analytics vendorUsing a data analytics vendor

in addition to our own resources

Using our own data warehouse and analysts

Figure 1

1990s. But the lessons learned— and not learned — throughTQM/CQI processes from 20years ago are applicable and vi-tal today. These processes re-quire healthcare leaders to looksystemically at people andprocesses, using facts (data) andutilizing continuous evaluationin order to improve quality. Thekey in turning a flood of infor-mation into actual usable, verifi-able program improvements isall about the basics of qualityimprovement. Even further back,core principles Deming pio-neered in the 1950s — taking asystems approach, trying to lo-cate the sources of variation inoutcomes, and separating outcontrollable from uncontrollablevariation — are arguably morecrucial now than ever. Our own data shows this is a

high priority for most: According

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What are your organization’s plans for the following IT projects over the next 24 months?

Already completed Will complete within 6 months Will complete within 12 months Will complete within 24 months Currently piloting Evaluating No plans

Base: 363 respondents working at a doctors’ practice, hospital, healthcare center or other healthcare providerData: InformationWeek 2013 Healthcare IT Priorities Survey of 451 business technology professionals, January 2013

R6430413/3

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IT Project PlansFigure 2

January 2014 6

January 2014 7

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to respondents to InformationWeek’s 2013Healthcare IT Priorities Survey, about a third(31%) of organizations had no plans to engagein a big data analytics project in the next 24months, while 21% were evaluating such plans.About 33% of organizations were already en-gaged 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 atyour objectives. The toolbox is the samewhether for clinical decision support, qualityand outcome measures, population health,or other data-driven program improve-ments. The foundation of a successful bigdata initiative applies regardless of whatdata repository (or repositories) you have,and which data analysis tools are used.

Planning The RevolutionFirst and foremost, determine who is ulti-

mately responsible for data flow and quality.Governance for data quality and program im-provement should reflect the overall organi-zational structure and touch every part of the

healthcare organization. It can’t be donepiecemeal. It truly is an “all in” endeavor.Formal organizational responsibility can be

held within one department or group — orseveral. That decision will probably be politicalas well as structural. However the formalstructure plays out, functionally there must bea level where “the buck stops here.” A specificperson or group makes sure that the right

data is collected at the necessary level of qual-ity and ensures that data gets analyzed andevaluated in meaningful ways, and that com-peting priorities in data collection and analy-sis 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 improv-ing quality and outcomes, reducing cost of

You’re Not as Agile as You Think

Every shop says it uses an Agiledevelopment process, but almostnone really does. Here are thethree reasons why, and steps toget back on track.

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Deploying a central, dedicated datawarehouse for use by analytic sys-tems will yield much better results

and allow for significantly greater flexibilityand capacity than trying to analyze a distrib-uted set of separate, independent data silos. The form, structure, and type of repository

will reflect the needs of the organization. Forinstance, translational research will dictate asignificantly different warehouse than a sys-tem whose primary goal is process and effi-

ciency improvements. Both may make use ofelectronic medical records, claims, pharmacy,labs, and billing information, but a transla-tional system will also tie in clinical trials andother research-related data sources. A repos-itory used for population health research willneed to pull in additional sources of infor-mation beyond those needed for the simpleoutcomes research and process improve-ment that a single facility might require.

—Richard Hoffman

Warehousing Data For AnalyticsDATA REPOSITORY

January 2014 8

services, improving efficiency, and so on. Theright person needs to ask, “With limited re-sources, where and how should we focus andcoordinate our efforts, and are there ways tosolve two, three, or more problems with onewell-placed process?” That team, committee,or other group has to be empowered to makethe necessary decisions and provide concreteguidance.As a core part of this work, both initial and

regular periodic strategic analyses ensure thatefforts and priorities in analytics — and the an-ticipated benefits of that analysis — all alignwith organizational mission, vision, and goals.In other words, the question of “what” data wecollect and analyze must go back to the fun-damental question of “why” we are doing allof this work in the first place. C-suite buy-inand involvement are essential at this level.

Playing The Political GameNext, you’ll need to put the right metrics in

place to evaluate that data. Involve the stake-holders and identify the specific key metricsthat will accomplish the “what” once the “why”

has been identified. Hands-on staffers need tobe fully involved in this process to make certain

that data obtained is valid and useful. This gets to the point of “how” the data will

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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.”

Importance of Healthcare IT Initiatives

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

R6430413/2

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Figure 3

January 2014 9

be collected, and often “when.” Staff mem-bers are usually very busy, so mandating extratime for collecting data is a tricky topic. Physi-cians and other direct care providers are al-ready impatient and annoyed by the time re-quirements of electronic health recordssystems. If the staff has to make a decision be-tween providing good patient care and creat-ing clean, usable data, patient care will (andshould) prevail. The success of your imple-mentation will hinge upon doing everything

necessary to avoid mak-ing the staff decide be-tween patient care andcreating data. Best prac-tices involve collectingpatient and care datafrom the staff whenthey aren’t actively pro-

viding hands-on care. The project leadersshould also work closely with staff before, dur-ing, and after implementation to maximizethe efficiency of the entire process. Be transparent that data quality will lead to

systemic program improvements. The more

that you communicate that the initiative hasobvious benefits and outcome improve-ments, the more the staff will be inclined tocooperate. Additionally, stress the balance be-tween the improvements that will come fromthe data collection while respecting the inter-est of making the fewest inconveniencesupon the staff as possible. If the staff doesn’twant to collect the data or doesn’t have anyreal buy-in about the why and the what, notto mention the how and the when, you canmake a pretty sure bet that the resulting dataquality will be low and/or inconsistent. Transparency and internal communication

can make or break the entire initiative. Behindthe scenes, actively involve your stakeholdersby providing a regular assessment of how thedata was used in actual program improve-ments. You can use that opportunity to detailwhether any ongoing changes in priorities orprocesses are necessary. While it seems like agiven, this technique prevents this entire ef-fort from being an expensive waste of timeand effort. Communicating the results, posi-tive or not, to all staff also serves to increase

confidence and improves organizationalmorale. Successes build confidence. Resist the urge to downplay setbacks —

frank discussion of failures makes it clear yourdata analytics has the highest level of atten-tion and oversight. A quality and program im-provement initiative that is an opaque “blackbox” will likely ultimately fail, no matter howwell intended. The worst result is that staffersbelieve — either correctly or incorrectly —that their efforts were allowed to become abottomless pit where resources go to die.Good projects often fail — and marginal onescan succeed — because of the amount of ef-fort given to internal communication and mar-keting. This is true of any complex project, butparticularly one that touches all aspects of thebusiness and is intended to bring substantialand transformative quality improvements.

Bad Data Kills Projects Bad data produces poor or meaningless re-

sults, no matter how much you massage it.Data quality is something that has to bebaked in from the start. Unfortunately, not

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According to our 2013 Healthcare IT

Priorities Survey, “meeting regulatory

requirements” was by far the highest

priority for respondents working at a

healthcare provider.

January 2014 10

many organizations have in-house expertisein achieving high data quality. It’s absolutelyworth hiring or bringing in top-notch data

quality expertise as early in your processes aspossible. You’ll avoid reengineering, retooling,or redesigning major systems at the very

least. You can also avoid the painful discoverythat your data, and the conclusions drawnfrom that data, are invalid years from now. Fol-

lowing the path of bad data isworse than flying blind — it’s likeflying while a totally fictional sceneis projected into the cockpit. Deci-sions made on the basis of thatdata will bear little to no resem-blance to what actually needs tohappen, and results will be no bet-ter than random chance.Interoperability has been a major

issue, and still is with some sys-tems. Getting access to a proprietydata source from your electronicmedical records or other core sys-tems for analysis can be a majorpain. Luckily, while Meaningful UseStage 2 means significant work formany healthcare organizations, theinteroperability requirements puthealthcare information systemvendors under the gun to betterenable data exchange. That’s good

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Which of the following IT projects are completed or will be completed within the next 24 months?

Planned IT Projects

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

R6430413/4

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2013 2012

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Figure 4

January 2014 11

news for healthcare data analytics as we getcloser to Stage 2 deadlines.The increasingly constant pace of change

can at times be made to work toward yourgoal of data-driven program improvement. Forexample, ICD-10 adoption is a major endeavorfor many healthcare organizations, large andsmall. Healthcare adminis trators are under alot of pressure and num erous headaches be-cause of the associated deadlines. But that same ICD-10 pressure and associ-

ated action items can be harnessed to pro-vide rationale for examining data collectionacross the board. If you need to do major or-ganizational surgery anyway, you might aswell take advantage of the opportunity to re-fine your processes while you’re in there.“Blame it on ICD-10” can be a successful strat-egy for change if there is significant internalresistance.Finally, delegate to a person or a group the

responsibility to anticipate and keep on topof trends in data collection and analysis.Healthcare data analytics continues to evolve,not only in terms of data repositories and

analysis tools, but also potentially disruptivefactors like always-on patient-facing mobile

data collection (“the iPhone effect”). Someoneor some group in your organization should al-

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Which vendor(s) is your organization using, or planning to use, for big data analytics?

Big Data Analytics Vendors

Oracle

IBM

InterSystems

Humedica

Atigeo

Explorys

Other

We’re still evaluating vendors

Don’t know

Note: Multiple responses allowedBase: 84 healthcare provider respondents using or planning to use a data analytics vendor for their big data analysis initiativeData: InformationWeek 2013 Healthcare IT Priorities Survey of 451 business technology professionals, January 2013

R6430413/6

24%

16%

5%

4%

1%

1%

16%

42%

13%

Figure 5

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ways be looking over the horizon, trying to di-vine what will show up with the next sunrise.Ideally, a cross-disciplinary tiger team of

medical staff, IT/technical staff, and data ana-lysts will devote regular time to assessing newand potentially disruptive developments indata collection and analysis. For an example,one of the biggest areas of coming disruptionis the idea of mobile data collection via pri-vately owned data-enabled devices. With smartphone adoption at an all-time

high and still growing, many patients havewhat is essentially an active telemetry deviceon their person at all times. Consumer-levelhealthcare data collection devices are alreadyactive on the consumer market, some at sub-$100 price points. Consider activity and exer-cise monitors such as Fitbit and Jawbone UP,or other wellness-related devices such assleep monitors. In early 2013, the FDA ap-proved the first wireless Bluetooth-enabledblood glucose monitoring system, which canupload data automatically to an iPhone. Asmall flood of similar inexpensive wireless-en-abled medical care devices is expected to hit

the market in 2014. As time marches forward,the consumerization of healthcare will gainsteam. There are obvious potential problemshere: data quality, privacy and confidentialityissues, interoperability, and so on. No matterhow you look at it, the potential for continu-ous monitoring of patients and real-time up-load of data is clearly a game-changer, and itis coming — that future is almost here.

Big Data In Big HealthOne thing is certain — the future will only

bring more and more data to your doorstep.You need to effectively analyze and use all ofthat data with an aim to continuously improveyour outcomes, processes, and operations. Thebetter foundation you can set now, the betterable you will be to handle the flood and turnit into a gold mine of useful information.Having peered into the near future, by look-

ing even further back into the past, we mayfind some wisdom that can help lead us tosuccess. More than 2,000 years ago Pythagorasshared sage advice in his Golden Verses: “Neverallow sleep to close your eyelids after you go

to bed until you have examined all your ac-tions of the day by your reason. In what have Idone wrong? What have I done? What have Iomitted that I ought to have done? If in thisexamination you find that you have donewrong, reprove yourself severely for it — andif you have done any good, rejoice.” As we speed ahead toward the fast-approach-

ing horizon of data-enabled program improve-ment, with solid proactive planning and duecare, using the right tools and developing solidstrategies for change, it can be hoped that weshall all have frequent cause to rejoice.

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