Department of Nursing, University of Massachusetts Boston
Management Team Facilitator, https://nursology.net
Preface: Bringing the Science of Winning to Healthcare
A few years before the publication of this book, I attended an international mathematics conference for research in simulation studies and predictive analytics. Out of more than 300 attendees, there was only one other attendee from healthcare. For three days there were presentations by researchers from the fields of logistics (trucking) and mining, reviewing how they used predictive analytics and simulation to proactively manage outcomes related to productivity and company output. Surely, I thought, the same kinds of mathematical formulas presented by the truckers and miners could be used in healthcare to move us from reactive use of data to a proactive approach.
Currently, hospitals evaluate outcomes related to falls and infections using hindsight‐based analytics such as case studies, root cause analyses, and regression analyses, using retrospective data to understand why these outcomes occurred. Once the underlying causes for the outcomes are identified, the organization creates action plans for improving the outcomes. The problem with this process is that retrospective data provides only hindsight, which does nothing to create a profile of current or future risk. Healthcare organizations typically stop short of supporting prospective management of the data, which would allow for the collection of meaningful data about real‐life trends and what is actually happening in practice right now. Conversely, the truckers and miners at the conference showed how predictive analytics can be used to study risk for the purpose of managing unwanted outcomes before they occur. Since I am both a data scientist and a nurse, I could see clearly that the formulas from the math conference could apply to healthcare; all you would have to do is specify the models.
This book is about how analytics—mostly predictive analytics—can be used to improve outcomes in healthcare. This book also reveals how good data, derived from good theory, good measurement instruments, and good data collection processes has provided actionable information about the patient, the caregiver, and the operations of care, which have in turn inspired structure and process changes that saved millions of dollars while improving the experience of both patients and providers.
Organizations that have embraced predictive analytics as a central part of operational refinement include Amazon, IBM (Bates, Suchi, Ohno‐Machado, Shah, & Escobar 2014), Harrah’s casino, Capital One, and the Boston Red Sox (Davenport 2006). In his 2004 book (and the 2011 film), Moneyball, Michael Lewis, documents an example of how in 2002 the Oakland A’s professional baseball team, which had the lowest payroll in baseball, somehow managed to win the most games. This paradox of winning the most games despite having the skimpiest budget in the league was due to an assistant general manager who used a baseball‐specific version of predictive analytics called sabermetrics to examine what combination of possible recruits would reach first base most reliably, and would therefore result in the team winning the most games. These recruits were not the most obvious players—in fact, they were not considered by almost anyone to be the best players. It was only predictive analytics that made them visible as the right players to comprise this winning team.
If predictive analytics can help a team win more games, why couldn’t they help patients heal faster? Why couldn’t they help clinicians take better care of themselves? Why couldn’t predictive analytics be used to improve every outcome in healthcare?
As a data scientist and operations analyst, it is my job to present data to healthcare leaders and staff members in a way that allows them to easily understand the data. Therefore, it is the job of this book to help people in healthcare understand how to use data in the most meaningful, relevant ways possible, in order to identify the smartest possible operational improvements.
For decades, the three editors of this book have been conducting research to measure some of the most elusive aspects of caring. This book provides instructions and examples of how to develop models that are specified to the outcomes that matter most to you, thereby setting you up to use predictive analytics to definitively identify the most promising operational changes your unit or department can make, before you set out to change practice.
List of Acronyms
A&OAlert and orientedACCFAmerican College of Cardiology FoundationACEAngiotensin‐converting enzymeACEIAngiotensin‐converting enzyme inhibitorAGFIAdjusted goodness of fit indexAHAAmerican Heart AssociationAMIAcute myocardial infarctionANEFAcademy of Nursing Education FellowANOVAAnalysis of varianceAPNAdvanced practice nurseARBAngiotensin receptor blockersARNIAngiotensin receptor‐neprilysin inhibitorASAMAmerican Society of Addiction MedicineAuto‐Falls RASAutomated Falls Risk Assessment SystemBNPBrain natriuretic peptideBSNBachelor of science in nursing (degree)BUNBlood urea nitrogenCACCoronary artery calciumCADCoronary artery diseaseCARICOMCaribbean Community (a policy‐making body)CATCaring Assessment ToolCBASCaring Behaviors Assurance System©CDIChoice Dynamic InternationalCFIComparative fit indexCCUCoronary care unitCDCCenters for Disease ControlCEOChief executive officerCESCaring Efficacy ScaleCFSCaring Factor Survey©CFS‐CMCaring Factor Survey – Caring of ManagerCFS‐CSCaring Factor Survey – Caring for SelfCFS‐CPVCaring Factor Survey – Care Provider VersionCFS‐HCAHPSCaring Factor Survey – hospital consumer assessment of healthcare providers and systems (a 15‐item patient/provider survey)CKDChronic kidney diseaseCLCentral lineCLABSICentral line‐associated bloodstream infectionCMSCenters for Medicare and Medicaid ServicesCNACertified nursing assistantCNOChief nursing officerCNSClinical nurse specialistCOPDChronic obstructive pulmonary diseaseCPMClinical Practice ModelCPRCardiopulmonary resuscitationCPSCaring Professional ScaleCRTCardiac resynchronization therapyCRT‐DCardiac resynchronization therapy defibrillatorCRT‐PCardiac resynchronization therapy pacemakerCVACerebrovascular accidentCQIContinuous quality improvementCVCCentral venous catheterCHCMCreative Health Care Management®DNPDoctor of nursing practiceDNRDo not resuscitateDNR‐BAllows aggressive care, but not to the point of cardiopulmonary resuscitationDVTDeep vein thrombosisEDEmergency departmentEFEjection fractionEFAExploratory factor analysisEKGElectrocardiogramEKG QRSA segment of the EKG tracingELNECEnd‐of‐Life Nursing Education ConsortiumEMRElectronic medical recordESCEuropean Society of CardiologyFAANFellow American Academy of NursingFTEFull‐time employeeGFRGlomerular filtration rateGLMGeneral linear modelGPUGeneral patient‐care unitGWTGGet With The Guidelines (measurement tool)HAIHospital‐acquired infectionHCAHealing Compassion AssessmentHCAHPSHospital Consumer Assessment of Healthcare Providers and SystemsHEEHealth Education of EnglandHESHealthcare Environment Survey (measurement instrument)HFHeart failureHMOHealth maintenance organizationICDImplantable cardioverter defibrillatorICUIntensive care unitIRBInstitutional review boardIVIntravenous or information valueI2E2 Inspiration, infrastructure, education and evidenceIOMInstitute of MedicineKMOKaiser–Myer–Olkin (mathematical tool)LOSLength of stayLCSWLicensed clinical social workerLVNLicensed vocational nurseLVSDLeft ventricle systolic dysfunctionMATMedication‐assisted treatmentMBEMember of the British EmpireMICUMedical intensive care unitMFSMorse Falls ScaleMLMachine learningMSNMaster of science in nursing (degree)MRNMedical record numberNANursing assistantNHSNational Health ServiceNHSNNational Healthcare Safety NetworkNICENational Institute of Health and Care ExcellenceNNMCNichols–Nelson Model of ComfortNT‐proBNPN‐terminal pro‐brain natriuretic peptideO2 OxygenOTOccupational therapist/occupational therapyOUDOpioid use disorderPCPalliative carePCAPatient care attendantPCIPercutaneous coronary interventionPICCPeripherally inserted central catheterPMTPacemaker mediated tachycardiaPNPneumoniaPCLOSEAn indicator of model fit to show the model is close‐fitting and has some specification error, but not very much.POLSTPhysician orders for life sustaining treatmentsPPCIProfessional Patient Care IndexPRPregnancy relatedPSIPerformance and safety improvementPSI RNPerformance and safety improvement registered nurseQIQuality improvementQRS(See
