Practical predictive analytics and decisioning systems for medicine : informatics accuracy and cost-effectiveness for healthcare administration and delivery including medical research

1111 Pages·2015·2.42 MB·English

by  Linda Miner

Practical Predictive Analytics and Decisioning
Systems for Medicine
Practical Predictive Analytics and
Decisioning Systems for Medicine
Informatics Accuracy and Cost-Effectiveness for Healthcare
Administration and Delivery Including Medical Research
Linda A. Winters-Miner, PhD Thomas Hill, PhD
Pat S. Bolding, MD Robert Nisbet, PhD
Joseph M. Hilbe, JD, PhD Nephi Walton, MS, PhD
Mitchell Goldstein, MD Gary D. Miner, PhD
Guest Chapter Authors:
GerardBritton,JD,MS LeslawKulach,MSc
EricW.Brown,PhD PiotrMurawski,MSc
JohnW.Cromwell,MD ChrisPapesh,MBA
DarrellDean,DO,MPH,CHCQM,FAIHQ VladimirRastunkov,PhD
JacekJakubowski,PhD DannyW.Stout,PhD
SvenKoch,RN,PhD ChristopherL.Wasden,EdD
MartinS.Kohn,MD,MS,FACEP,FACPE
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Dedication
This book is dedicated to the Guest Tutorial Authors,
in gratitude for all their expertise and hard work
in providing the central part of this book.
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Foreword by Thomas H. Davenport
I am pleased that you have purchased or picked up this book, because it covers one of the most important topics in
health care and human society overall. What is more important than using data and scientific evidence to improve
health care? As the many authors demonstrate throughout this volume, their topic has implicationsfor notonly ourper-
sonalhealth,butalsoourfinances,ourhappiness,andourlegacyasacountryandsociety.
Still, I suspect asyou gaze upon this hefty tome you might find it a little overwhelming. Even if you’re an expert in
the field ofhealthcare informaticsand analytics,there is likely tobemuch inthisbookthatyoudon’t know about. You
mayevenfeelthattheterm“practicalpredictiveanalytics”issomethingofanoxymoron.
However, dear reader, do not be dissuaded from diving into this book because of these concerns. First, the authors
of most chapters are careful to define their terminology and stay away from highly technical expositions. An educated
layperson can learn much from the book, and experts will have no problem with the great majority of chapters. The
toneisrelativelylightinmostchapters(atleast,giventheveryserioussubject),andthereareevenafewcartoons!
Secondly, the topics and terms are highly related. The reader comes to understand the relationships between and
among various types of healthcare analytics, informatics, and tools and approaches for medical decision-making. Upon
reading several chapters I realized, for example, that “predictive analytics” and “personalized medicine” are two sides
ofthe same coin. These are two terms thatI’d been familiar with for awhile, butInever thoughtabout their closerela-
tionship. Predictive analytics is simply the way we achieve personalized medicine; we are predicting how a particular
patient will respond to a medicine or treatment, given that person’s genotype and phenotype. It follows that the person-
alizationofmedicineisaprobabilisticactivityjustlikeanyotherformofpredictiveanalytics.
Thirdly, the book’s chapters are replete with examples, and two entire parts (cid:1) the second with tutorials, and the
third with case studies (cid:1) contain rich detail on how these ideas can be implemented in particular settings. I found the
casestudiesparticularlyuseful because theyinclude factorsthatgowellbeyond thetheoryofpredictiveanalysis inthat
particular domain. Many address issues such as culture, resistance by doctors and other practitioners, regulatory con-
straints,andotheraspectsoftheinstitutionalcontextinwhichpredictiveanalyticsareappliedtohealthcare.
Finally, you may find the volume of content in the book to be rather intimidating, as did I. However, I realized that
the notions of personalization and prediction have relevance in navigating this book as well. Not all the content and
chapters will be of equal interest to all readers. In a perfect world, we’d have a predictive analytics-based “configura-
tor” that would tailor the content to each individual reader. Short of that, however, the Contents list can serve as your
contentpersonalizationengine.Iencourageyoutouseitaggressively.
In fact, I confess not to have perused all of the chapters herein, but I have checked out most of them. I can safely
assure that they represent a high level of content quality on this most important of topics. Healthcare professionals,
quantitativeanalysts,andanyoneinterestedinthefutureofmedicinewillbeconsultingitformanyyearstocome.
ThomasH.Davenport
President’sDistinguishedProfessor,BabsonCollege;
Fellow,MITCenterforDigitalBusiness;
Co-founderandDirectorofResearch,
InternationalInstituteforAnalytics.
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Foreword by James Taylor
Those of us working in analytics and decision management have long regarded medicine as a prime target area. Like
many industries, medicine is seeing an explosion in available data as medical records are digitized, medical equipment
monitorspatients inreal time,andmedicalresearchisprovided inmachine-readableformats. Ifthesedatacan beeffec-
tively applied to make better decisions, then lives as well as money can be saved. Applying predictive analytics and
decision management to diagnosis, treatment selection, cost management, fraud detection, and more will result in con-
sistent, effective, cost-efficient medical outcomes. These decisions will be based not only on medical research and the
skills of medical professionals, but also on the analysis of an extensive and rapidly growing body of data. A book on
predictiveanalyticsanddecisioningsystemsinmedicine,then,isbothtimelyandnecessary.
This book provides an introduction to both the healthcare industry and the predictive analytic process. It discusses
the ongoing digitization of electronic medical records and how to use these systems, as well as the regulatory and
research framework within which medicine operates today. A wide-ranging collection of examples shows the potential
for predictive analytics and decisioning, while dozens of tutorial examples walk through real-world uses of predictive
analytics in medicine. Critically, throughout the book, the authors keep the reader focused on the need to use data to
improvedecision-making,notjusttomakepredictions.
There is a deep, across-the-board need for predictive analytics and more advanced decisioning systems in health
care.Thisbookoffersacomprehensivelookatthechallenges,opportunities,techniques,andtechnologies.
JamesTaylor
CEODecisionManagementSolutions
AuthorofDecisionManagementSystems:APracticalGuide
toUsingBusinessRulesandPredictiveAnalytics(IBMPress,2011).
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Foreword by John Halamka
At Beth Israel Deaconess Medical Center in Boston, I oversee 3 petabytes of healthcare data. Big Data is no longer
“big,” since I can easily store, protect, and curate multiple petabytes growing at 25% per year. My challenge is turning
dataintoinformation,knowledge,andwisdom.
Providing analytics and decision support to clinicians depends on good data, the appropriate business intelligence
tools, and actionable visualizations. This book is a primer and roadmap for the entire process of supporting care with
predictiveanalytics.
It includes an important discussion of healthcare data capture, highlighting variations in data quality and the useful-
ness of data gathered by different people in different workflows. Case studies illustrate how data can change care,
gettingtherightcaretotherightpatientsattherighttime.
The book also captures major trends, such as mobile technologies to gather patient-generated data and provide
alerts/reminderstoproviders.Itincludesemergingtechnologiessuchasthetransformationofunstructureddatatostruc-
tureddatausingnaturallanguageprocessingandinferencesuchasIBM’sWatson.
Healthcare IT industry pundits believe the next 5 years will belong to social networking, mobile, analytics, and
cloud technologies. This book provides practical advice that helps navigate many of these trends as we all work to
implementhealthcarereform,supportedbypredictiveanalytics.
JohnHalamka,MD,MS
ProfessorofMedicineandChiefInformationOfficer attheBethIsraelDeaconessMedicalCenter,
ChiefInformationOfficer andDeanforTechnologyatHarvardMedicalSchool,
ChairmanoftheNewEnglandHealthElectronicDataInterchangeNetwork(NEHEN),
CEOofMA-SHARE(theRegionalHealthInformationOrganization),
ChairoftheUSHealthcareInformationTechnologyStandardsPanel(HITSP),
andmemberoftheBoardoftheOpenSourceElectronicHealthRecordAgent
AuthorofTheBestofCP/MSoftware(Longman,1984);RealWorldUnix(Longman,1984);
andEspionageintheSiliconValley(Longman,1985),andoftheblog
Geekdoctor:LifeasaHealthcareCIO(http://geekdoctor.blogspot.com/).
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Preface
Everyone is fascinated by predictions; everybody wants to know the future. Some people go to psychics, some go to
mystics, others go to philosophers, to soothsayers, to television commentators, to a Nostradamus quatrain, or even to
theancientIncas.Recently,anewsourceofpredictionshasriseninourperceptions(cid:1)PredictiveAnalytics.
What is different about predictive analytics, compared with the many other claims of future knowledge? Predictive
analytics is based on relatively objective analyses of past data, following the principle that the best guide to the future
is the past. All other predictions of the future are based on highly subjective (maybe even fraudulent) systems that their
practitionersclaimarepredictiveof(oratleastrelevantto)thefuture.
Whyisthisdistinctionsoimportant?
First, it is impossible to know the future with certainty. Even those future events that we believe are absolutely
certain can be thwarted from some previously unknown influence. We can’t know everything in the present, and we
certainlycan’tknowanythingforcertaininthefuture.
Secondly,predictive analytics can expose new events thatmight happen in the future. The most intelligent approach
topreparingforfutureeventsistostudypastevents,andbepreparedforthemtohappenagain.But,whatifnewevents
happeninthefuture?Noothersystemcanpredictnewevents,withanydegreeofcredibility.
Thirdly, predictive analytics allows different people to look at the same information, predict new events in the
future,andcomparetheirfindings.Thesepredictionscanbeanalyzedaccordingtotheirassumptions,methods,andout-
comeprobabilitiestodecidewhichoutcomeisthebestpredictionofthefuture,evenifwecan’tknowforcertain.
MODERN MEDICINE: AN EXERCISE IN PREDICTION AND PREPARATION
Modern medical treatment is composed of two general activities: (1) diagnosis, and (2) treatment. Both of these activi-
ties involve predictions. In the past, some medical practitioners based their predictions on naturopathic and nutrition
philosophies. More modern practitioners base their predictions on results from the pharmaceutical industry’s clinical
trials. Some practitioners may trust the government to digest all of the information and clinical trial outcomes, and
mandate the acceptable course of action. Other practitioners, such as medical researchers, just want to do it themselves,
creatingtheirownknowledge.
This book is based on the premise that the future becomes evident (as far as we can know it) by careful analysis of
patterns in past data. This past evidence can provide trajectories of change in medical conditions and outcomes. For
example, a tree that bends toward the sun at its apogee in the sky during the active growing season will continue to
grow in that direction; that is the way living things in the world work. We can mark the starting point of growth, mea-
sure the angle and rate of tree growth, and (barring some catastrophic event) we can calculate where the top of the tree
willbein20years,andhowmuchwooditwillprovideifharvested.
Topol (2012) maintained that the starting point for human responses is the individual genome; from that point, our
medical lives can be predicted. In this book, we will show how genomic information can be analyzed to predict a
bewilderingvarietyofmedicaloutcomes.
Regardless of the starting point, we must follow some methodologies for analysis of past evidence in order to
make predictions of any future medical outcome (cid:1) but, these methodologies must all be compatible with each other.
Arecent issueof theAARP Bulletinposed this ratherstartlingobservation:
Ifhomebuildingwerelikehealthcare,carpenters,electricians,andplumberseachwouldworkwithdifferentblueprints,each
withverylittlecoordination...
(AARP,2012)
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xxii Preface
The resulting building constructed with such a disorganized methodology would not be very useful, nor would it
persist for very long. But our health care system is like that! Many patients find their healthcare systems rather opaque,
disorganized, and hard to navigate in order to produce acceptable outcomes. This situation in American health care
mustchange.
WASTED COSTS IN AMERICAN HEALTHCARE SYSTEMS
Our healthcare system is not only disorganized and inefficient; it is also very costly, and a significant amount of the
costiswasted(AARP,2012).Someofthesewastedcostsinclude:
(cid:1) $210billion(cid:1)Unnecessaryservices
(cid:1) $190billion(cid:1)Insuranceoverheadsandbureaucraticcosts
(cid:1) $130billion(cid:1)Preventableerrors/mistakes
(cid:1) $105billion(cid:1)Excessiveprices
(cid:1) $75billion(cid:1)Fraud
(cid:1) $55billion(cid:1)Missedpreventionopportunities.
This book provides many useful methodologies that can be orchestrated to work together in future medical decision
systems for building consistent medical outcomes without major cost wastage. This book also provides many tools for
usebymedicalpractitionerstomakeaccuratepredictions.Thefocusofthesepredictionsisonanythingrelatedtomedi-
calorhealthcare.Youmightbeapracticing physicianseekingcorrect andaccurate medical outcomes, oryoumightbe
ahospitalstaffmemberwho mustdemonstrate safetytothe accreditation organizations,oryou might bean analyst ina
healthcareinsuranceorganization whowantstopredictthefrequencyandseverityoflossrisk.Forallofthoseactivities
(and many more), Practical Predictive Analytics and Decisioning Systems for Medicine provides the data and the
step-by-steptutorialsthatwillputpowerintothehandsofpractitioners.
REFERENCES
AARP,2012.Caringforthecaregiver.AARPBull.53,9.
Topol,E.,2012.TheCreativeDestructionofMedicine:HowtheDigitalRevolutionWillCreateBetterHealthCare.BasicBooks,NewYork,NY.
About the Authors
Linda A. Winters-Miner, Ph.D., earned her bachelor’s and master’s degrees at
UniversityofKansas,herdoctorateattheUniversityofMinnesota,andcompletedpost-
doctoralstudiesinpsychiatricepidemiologyattheUniversityofIowa.Whileshe,with
her husband Gary Miner, raised their children, Becky and Matt, she spent most of her
careerasaneducator,inteachereducationandstatisticsandresearchdesign.Shespent
nearly two years as a site coordinator for a major (Coxnex) drug trial. For 23 years,
Miner directed academic programs for Southern Nazarene University(cid:1)Tulsa. Her pro-
gram directionincluded threeundergraduate programs in businessand psychology and
three graduate programs in management, business administration, and health care
administration.Shehasauthoredorco-authorednumerousarticlesandbooksincluding
with Gary and others, and the first book concerning the genetics of Alzheimer’s:
Alzheimer’s disease: Molecular genetics, clinical perspectives and promising new
research. Linda authored some of the tutorials in the first two predictive analytic books published in 2009 and 2012 by
Elsevier.Atpresent,sheteachesbothundergraduatestatistics&researchatSNU-Tulsa,teachesstatisticsandpredictiveana-
lyticsfortheIHIFamilyPracticeMedicalResidencyprograminTulsa,andalsoteachespredictiveanalyticsonline,includ-
inghealthcarepredictiveanalytics,forboththeUniversityofCalifornia(cid:1)IrvineandUniversityofCalifornia(cid:1)SanDiego.
PatBolding, M.D.,F.A.A.F.P.isa practicingboard certified familyphysician. Hehas
usedanEMR(ElectronicMedicalRecord)sincehisresidencytraininginthemid1980s
(cid:1)atthetimeitwasthe“pioneering”TechniconMedicalInformationSystem.Later,as
theCEOofalargefamilypracticegroup(whichalsohosteda30-residenttrainingpro-
gram),heledtheselectionandimplementationofseveralEMRsystems,beginningwith
the text-based Medic Autochart then Misys EMR and finally the A4-Healthmatics sys-
tem.In2007,hejoinedamulti-specialtygrouppractice/integrateddeliverysystemwhere
he serves on the EMR committee that oversaw the implementation of the NextGen
ambulatory EMR. More recently he was a member of the search committee that chose
the Epic system to replace NextGen. He is a frequent speaker on health/medical topics
andhasaspecialinterestinevidence-basedmedicine.Heisanadjunctfacultymember
ofSouthernNazareneUniversity,teachingintheHealthCareMBAprogram.
Thomas Hill, Ph.D., received his Vordiplom in psychology from Kiel University in
GermanyandearnedanM.S.inindustrialpsychologyandaPh.D.inpsychologyand
quantitativemethodsfromthe UniversityofKansas. He wasassociate professor(and
then research professor) at the University of Tulsa from 1984 to 2009, where he
taught data analysis and data mining courses. He also has been vice president for
Research and Development and then the Analytic Solutions section at StatSoft Inc.,
where he has been involved for over 20 years in the development of data analysis,
dataandtextminingalgorithms,andthedeliveryofanalyticsolutions.Dr.Hilljoined
DellthroughDell’sacquisitionofStatSoft(www.StatSoft.com)inApril2014,andhe
is currently the Executive Director for Analytics at Dell’s Information Management
Group.Dr.HillhasreceivednumerousacademicgrantsandawardsfromtheNational
Science Foundation, the National Institute of Health, the Center for Innovation
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