Magdalene M. Assimon|Jennifer E. Flythe
Thirty-Day Hospital Readmissions in the Hemodialysis Population
University of North Carolina at Chapel Hill|University of North Carolina at Chapel Hill
problem well put halfsolved john dewey 18591952 recent years united states policymakers made 30day hospital readmission reduction centerpiece efforts curb medicare costs general medicare population 15 patients readmitted hospital within 30 days discharge 1 unplanned rehospitalizations cost 20 billion per year 2 fiscal year 2013 medicare began levying financial penalties hospitals higher expected readmission rates via affordable care acts hospital readmissions reduction program readmission problem even dire among individuals receiving maintenance dialysis 30day hospital readmission rates 35 vulnerable group 3 incentivize readmission reduction dialysis population centers medicare medicaid services implemented standardized readmission ratio pay performance quality measure outpatient dialysis clinics 2017 performancebased policies created urgency broader united states health care system dialysis community prompting efforts describe quantify readmissions importantly identify modifiable populationspecific risk factors using variety data sources investigators identified range readmission risk factors hemodialysis population patientrelated factors including comorbid conditions heart failure 4 myocardial infarction 4 malignancy 45 depression 6 biochemical markers anemia 7 hypoalbuminemia 5 dialysisrelated factors shorter vintage 8 catheter vascular access 5 associate increased 30day readmission odds hospitalrelated factors including prior hospitalization history 5 weekend day discharge 5 also associate increased readmission odds finally providerrelated factors erythropoietinstimulating agent adjustment 7 frequent provider visits posthospitalization 9 associate decreased readmission odds although identified risk factors readily modifiable point highrisk subpopulations potentially target interventions issue clinical journal american society nephrology chan et al 10 provide insights 30day unplanned readmission rates predictors among united states patients hemodialysis without medical insurance using data 2013 nationwide readmissions database nrd publicly available data source contains information community hospital discharges 21 states authors 1 describe rate 30day unplanned hospital readmissions 2 evaluate concordance primary discharge diagnoses index hospital admissions subsequent readmissions 3 identify predictors readmission among individuals receiving maintenance hemodialysis 390627 eligible index hospitalizations identified 87302 22 resulted least one unplanned 30day readmission primary causes index hospitalizations corresponding readmissions basis agency healthcare research quality clinical classification software ccs categorization often disparate approximately 80 index hospitalizationreadmission pairs discordant primary discharge diagnoses notably patients three readmissions 30day period index hospitalization constituted 2 patients accounted 20 readmissions several comorbid conditions eg drug abuse history heart failure diabetes depression associated increased 30day readmission odds study strengths include large sample size use nrd unique data source facilitated assessment 30day unplanned readmissions hemodialysis cohort without restriction insurance status authors used nrdbased population weights generalize 21state sample nationally representative cohort inclusion patients without insurance ability distinguish elective nonelective admissions differentiate nrd medicarebased us renal data system usrds commonly used united states dialysis data repository chan et al 10 acknowledge several study limitations including lack data previously identified hospital readmission predictors race dialysis vintage vascular access type missed dialysis treatments laboratory results hospital discharge day additionally categorization diagnosis codes ccs groups may misclassified related index hospitalizationreadmission discharge diagnosis pairs discordant example index hospitalizations acute myocardial infarction ccs code 100 considered discordant readmissions chest pain ccs code 102 finally nrd contain information competing risk hospital death precluding appropriate analytic handling individuals died within 30 days index hospitalization discharge without readmission patients could identified nrd classified readmission data limitations highlight challenges using single data source studying complex multifaceted phenomenon hospital readmission date majority investigators relied administrative claimsbased usrds study readmissions among united states patients hemodialysis 48911 others used electronic health record ehr data including dialysis provider hospital ehr databases 57 study results must always considered context data source limitations example administrative data like usrds nrd collected billing reimbursement purposes may diagnostic outcome validity shortcomings affect clinical risk prediction addition administrative data sources contain information known readmission risk factors including dialysis treatment characteristics laboratory test results comorbid condition severity socialbehavioral characteristics compared administrative claims data ehr data granular less likely influenced reimbursementrelated coding decisions ehrs offer highly detailed account patients clinical data medical histories ehrs united states standardized making data synthesis across health care systems difficult additionally provider notes ehrbased text fields may contain pertinent prognostic information however extracting meaningful information unstructured data large scale challenging requires specialized tools natural language processing software hospital ehrs include key information hospitalbased complications well laboratory diagnostic test results typically lack detailed information longitudinal esrd care outpatient dialysis treatments dialysis provider ehrs however contain outpatient dialysisrelated information laboratory results unfortunately dialysis provider ehrs link hospital ehrs preventing simultaneous consideration potential outpatient inpatientderived readmission risk factors finally ehr systems offer potential warehousing patientreported data health literacy social support factors shown associate readmission among patients dialysis 6 however data captured routinely greater attention patientreported data collection needed although multiple observational studies identified 30day hospital readmission risk factors using diverse data sources surprisingly little published data evaluating interventions designed reduce readmissions wingard et al 12 recently reported findings nonrandomized intervention included structured posthospital followup care coordination centralized call system provider information exchange 26 dialysis clinics authors showed modest overall 30day readmission declines found statistical difference pre postintervention readmission change intervention control clinics assessing effect reimbursement policies readmissions erickson et al 11 found 2004 medicare payment reform incentivizing face face provider visits patients dialysis affect allcause readmission rates somewhat disappointing findings underscore need innovative integrative data analytics readmission risk modeling greater emphasis testing refining readmission prevention strategies model improvement suggests using series rapid plandostudyact pdsa cycles test adapt health care change interventions directed readmission reduction populationspecific data integral feature pdsa steps used plan analyze iteratively refine interventions figure 1 given noted limitations singledata source reliance multiple opportunities improve risk prediction dialysis population example integrating data multiple sources including administrative claims inpatient outpatient ehrs supplementing traditional sources unique linkages data wearable healthmonitoring devices us census national centers environmental information would facilitate simultaneous consideration array potential patient dialysis hospital provider socioeconomic environmentalrelated readmission risk factors additionally application advanced predictive analytics including realtime risk modeling machine learning may improve risk prediction ultimately facilitating individualized service provision hospital dischargefigure 1 using diverse integrated data multiple sources inform steps pdsa quality improvement action cycles may improve risk prediction intervention implementation refinement ultimately reduce 30day hospital readmissions among individuals receiving maintenance dialysis ehr electronic health record pdsa plandostudyactthus far quest reduce 30day hospital readmissions among patients dialysis nephrology community spent much time planning phase however pdsa cycles action essential make inroads esrd readmission problem must implement analyze improve interventions accepting pdsa cycles iterative require continuous refinement must informed population systemspecific data fortunately anticipate emerging data integrated care models like esrd seamless care organizations highly informative accelerate rapid cycle quality improvement directed reducing readmissions findings chan et al 10 others shed light potential readmission reduction strategies bring forward phase examples include 1 targeting individualized discharge services select subpopulations depression substance abuse poor social support low health literacy 2 performing timeupdated risk stratification accounting changes patients medical social functional status 3 establishing collaborative data exchanges enhance communication across health care providers settings american philosopher john dewey said problem well put halfsolved problem 30day hospital readmissions among individuals receiving maintenance hemodialysis increasingly well put next step see problem also well solved disclosures mma jef received investigatorinitiated research funding renal research institute new york new york subsidiary fresenius medical care north america jef received speaking honoraria dialysis clinic incorporated nashville tennessee renal ventures denver colorado american renal associates beverly massachusetts american society nephrology washington district columbia baxter deepark illinois national kidney foundation new york new york multiple universities