This paper is intended to give an overview of Knowledge Discovery in Large Datasets (KDD) and data mining applications in healthcare particularly as related to the Minimum Data Set, a resident assessment tool which is used in US long-term care facilities. The US Health Care Finance Administration, which mandates the use of this tool, has accumulated massive warehouses of MDS data. The pressure in healthcare to increase efficiency and effectiveness while improving patient outcomes requires that we find new ways to harness these vast resources. The intent of this preliminary study design paper is to discuss the development of an approach which utilizes the MDS, in conjunction with KDD and classification algorithms, in an attempt to predict admission from a long-term care facility to an acute care facility. The use of acute care services by long term care residents is a negative outcome, potentially avoidable, and expensive. The value of the MDS warehouse can be realized by the use of the stored data in ways that can improve patient outcomes and avoid the use of expensive acute care services. This study, when completed, will test whether the MDS warehouse can be used to describe patient outcomes and possibly be of predictive value

Abbott, Patricia A.

Adya, Monica

Canfield, Kip

Manchand, Roopak

Quirolgico, Stephen

English

epublications@Marquette

Marquette Universitye-Publications@MarquetteManagement Faculty Research and Publications Management, Department of1-1-1998Can the US Minimum Data Set Be Used forPredicting Admissions to Acute Care Facilities?Patricia A. AbbottUniversity of Maryland School of NursingStephen QuirolgicoUniversity of Maryland Baltimore CountyRoopak ManchandUniversity of Maryland School of NursingKip CanfieldLaboratory for Healthcare Informatics - University of Maryland Baltimore CountyMonica AdyaMarquette University, monica.adya@marquette.eduPublished version. Published as part of the proceedings of the conference, Medinfo '98 : proceedings ofthe Ninth World Conference on Medical Informatics : "Global health networking : a vision for the nextmillennium", Seoul, 1998, 1998, 1318-1321. DOI. © 1998 IOS Press. Used with permission.Dr. Adya was affiliated with University of Maryland at the time of publication.MEDINF098B. Cesnik et ale (Eds)Amsterdam: IDS Press© 1998 IMIA. All rights reservedCan the US Minimum Data Set Be Used for Predicting Admissions to Acute CareFacilities?Patricia A. Abbott, MS, RNC a-b, Stephen Quirolgico, .MS, Doctoral Candidate", Roopak Manchand",MSc, Kip Canfield, PhDd, Monica Adya, PhDb"University ofMaryland School ofNursing,bUniversity ofMaryland Baltimore County,'University ofMaryland Schoof.ofMedicine,dLaboratory!or Healthcare Informatics - University ofMaryland Baltimore CountyAbstractThis paper is intended to give an overview ofKnowledge Dis-covery in Large Datasets (KDD) and data mining applicationsin healthcare particularly as related to the Minimum Data Set,a resident assessment tool which is used in US long-term carefacilities. The US Health Care Finance Administration, whichmandates the use of this tool, has accumulated massive ware-houses of MDS data. The pressure in healthcare to increaseefficiency and effectiveness while improving patient outcomesrequires that wefind new ways to harness these vast resources.The intent of this preliminary study design paper is to discussthe development ofan approach which utilizes the MDS, in con-junction with KDD and classification algorithms, in an attemptto predict admission from a long-term care facility to an acutecare facility. The use ofacute care services by long term careresidents is a negative outcome, potentially avoidable, andexpensive. The value ofthe MDS warehouse can be realized bythe use ofthe stored data in ways that can improve patient out-comes and avoid the use ofexpensive acute care services. Thisstudy, when completed, will test whether the MDS warehousecan be used to describe patient outcomes and possibly be ofpredictive value.KeywordsKnowledge Discovery in Large Databases; Classification; Min-imum Data Set; Nursing Informatics,IntroductionAdvances in database technology in concert with the push forcomputerized patient records have enabled healthcare organiza-tions to collect and store data at rates unimaginable decadesago. Unfortunately, this headlong rush into the capture and stor-age of patient related data has led to massive data warehousesthat have become unwieldy for purposes of analysis. Thisconundrum is not specific to healthcare; in reality, many otherfields such as banking, industry, fmance and manufacturing arefaced with identical problems. Fayyad [1] in describing theprocess of capturing data for analysis from the torrents of dataentering warehouses likened it to "drinking from a fire hose".Indeed, the issue at hand is how to integrate, harness and ana-1318lyze the massive amounts of data that are entering and beingstored in large data repositories.KDD ProcessThe techniques used in KDD and data mining are not new, hav-ing been discovered in the field of Artificial Intelligence (AI)research in the 1980's. However, these techniques are just nowgaining popularity in the analysis of large datasets in healthcare.Several factors have contributed to convergence, such as thewidespread availability of scaleable information technology,the dawning awareness of the value of information, and wide-spread deployment ofhigh volume integrated health care infor-mation systems. Prior to this confluence, the vast majority ofhealth related data was stored in disparate data silos with (inmany instances) limited capacity.The move towards integrated health systems and the tracking ofdata from "cradle to grave" has highlighted the need for amethod by which the vast amounts of data can be analyzed andvisualized. KDD can be used to sift through large repositories to"discover" trends, predictive patterns, or correlation's in data;confrrm hypotheses; and highlight exceptions. This functional-ity becomes critical in the light of intense competition for scarcehealthcare resources. KDD and data mining allows organiza-tions to begin to make sense of the data being collected. Failureto do so increases the risk of surrendering the value of the datathat enormous amounts of resources are being devoted to cap-ture.Fayyad, Piatetsky-Shapiro, and Smyth [2] define KDD as the"non-trivial process of identifying valid, novel, potentially use-ful, and ultimately understandable patterns in data." Accordingto Goodwin [3] data mining and KDD in healthcare uses a"combination of artificial intelligence and computer sciencetechniques to help build knowledge in complex health caredomains." In essence, KDD can be viewed as extracting high-level knowledge from low level data [1]. KDD uses discovery-based approaches in which pattern recognition and matching,classification or clustering schemas, and other algorithms areused to detect key relationships in the data. The ability to "dis-cover" facts separate from the original hypothesis is viewed as avalue-added activity.1319A, AbbottKDD in HealthcareIn practical terms related to healthcare, the use ofKDD and datamining holds much promise. Studies have shown that the over-load of data provided to healthcare providers can inhibit thedecision making process [4]. With the advent and increasingfrequency of computer based patient record initiatives, provid-ers are being faced with the potential for drowning in patientdata. While it is commendable that the computerization ofhealth data is moving forward, the inability to organize, access,and analyze large datasets is troubling. Providers, administra-tors, and organizations require methods such as KDD to be ableto filter the "essence from the bulk". In other words, the resultsofthe classification resulting from KDD and data mining can beincorporated into clinical systems to assist clinicians in trans-forming voluminous amounts of data into comprehendibleinformation.Ofparticular interest to these authors is the Health Care FinanceAdministration (HCFA) resident assessment instrument (RAI),-based on the Minimum Data Set (MDS), for patients in long-term care (LTC) facilities. The MDS is a patient assessmenttool with over three hundred data points per assessment periodwhich is collected by all LTC facilities in the US that partici-pate in Medicare/Medicaid programs. The MDS is adminis-tered quarterly, and more often if marked change in thepatient's status is evidenced. Two studies of particular impor-tance focused upon the use of the MDS in US long term carefacilities [5]. The results of these two studies demonstrated apositive impact in three separate areas; resident outcomes, proc-esses of care, and administrative perspectives. The MDS wasshown to be an excellent outcome measurement tool in whichdata can be collected, aggregated and analyzed to examineprocesses of care.MDSThe Omnibus Budget Reconciliation Act (OBRA) of 1987 con-tained Nursing Home Reform Amendments. The Amendmentsimposed new rules for nursing home care, requiring that long-term care facilities which participate in Medicare and Medicaidprograms perform periodic assessments of the functional capac-ity of individual residents. OBRA required the establishment ofa minimum data set by which the functional capacity of resi-dents could be measured.This minimum data set (MDS) was to be composed of a set ofcore elements and common definitions for use by the LTC facil-ities. In addition, guidelines were established for use of theMDS and an assessment instrument was designated. In 1992,HCFA proposed as a requirement for participation in the Medi-care and Medicaid programs that facilities encode the MDS in amachine-readable form capable of reporting MDS data toHeFA on a routine basis. [6] The original deadline for compli-ance with this mandate was October 1, 1994. RCFA anticipatedthat a national MDS collection would:1. foster.the growth of a management information systemsin LTC's which would contribute to an improvement inthe cost-effectiveness of LTC;2. identify effective care patterns which would facilitatetrends, highlight the use of resources, assist in the evalu-ation of policy and program options in long-term caredelivery and financing, and promulgate national andregional analyses. In particular, the interests included:case-mix payment, patterns, organizational structure,and physician participation.3. encourage quality monitoring by focusing on targetedreviews which would increase the chances of detectingproblem facilities. The collection of large amounts ofdata from the MDS would also facilitate the develop-ment ofnationwide patient norms by which all LTC res-idents could be measured. Once aggregated by facility,the use of the data sets could be used for aggregate anal-ysis and comparisons at state, regional and national levels.The use of the MDS has met with limited success in LTC facili ..ties. A noted failure of the MDS is the lack of longitudinal andcross-sectional feedback to clinicians. ThIs contributes to theperception of the MDS as additional administrative burdenrather than a clinical tool for improving or measuring patientoutcomes. As noted previously, the MDS is an excellent assess-menttool in which the effect or processes of care can be exam-ined both longitudinally and cross-sectionally. The benefits tobe gained by benchmarking the MDS both within and acrossfacilities, regions, and healthcare systems are tremendous. Thethree major challenges to be overcome include: the mining ofdata required for research from the MDS warehouse, the abilityto properly classify or predict clinical trends, and the need toreturn the information essence back to clinicians and adminis-trators in LTC facilities.Acute Care and LTC ResidentsThe use of acute care services by LTC residents is a negativeoutcome, potentially avoidable, and expensive. .Previousresearch in elder patient care has shown that admission to anacute care facility from a LTC results in a 59% increase in com-plication rates [7], a three-fold increase in hospital morbidityand mortality [8], and a general worsening of psychiatric statusin 40% of patients [9]. Although many admissions to an acutecare facility cannot be avoided, early detection and interven-tions aimed at minimizing hospitalization are of critical impor-tance in this frail population. The value of the MDS warehousecan be realized by the use of the stored data in ways that canimprove patient outcomes and possibly avoid the use of expen-sive acute care services.In order to effect the rate of hospitalization, we must first beable to measure and predict risk. Measuring and predicting riskcan be used as a clinical decision support resource to impactupon provider behaviors and care patterns. TIle prediction ofrisk of admission to an acute care facility from a LTC may bebased upon a comprehensive patient assessment that addressesclinical, functional, and interventional components. In addition,factors specific to the LTC such as ownership, bed size, types ofspecial units, and category/numbers of staff require investiga-tion in relation to their contribution to admission risk.'It is premised that with the use of Knowledge Discovery inLarge Datasets (KDD) procedures and connectionist machinelearning techniques, the data in MDS warehouses can be inves-1320Aspects ofPatient Caretigated and the assessment of risk of admission to an acute carefacility from a LTC can be performed. The assessment and sub-sequent alerts to providers of admission risk can be used to sig-nal that processes of care may need attention, with the potentialfor improvement ofpatient outcomes.Due to the enormity of the RCFA MDS data warehouse, theeffort to set the parameters, extract, and clean data is daunting.The goals (as set forth by HCFA) are contingent upon the readyaccess and analysis of data stored by RCFA in the MDS files.The immensity of the warehouse is a challenge, requiringadvanced mining, analysis and classification work to completethe expectations and .alter user perceptions as mentioned in theprevious section.Current StudyThe selection of the target data from the massive MDS ware-house required the setting of several inclusion factors. Theinclusion criteria included:1. the presence ofthree ofmore MDS assessments in thespecified time period; assessments for the 1993-1995time period.2. facilities with a bed size of 500 or larger and located in asimilar geographic region.These criteria were necessary to reduce the data set to managea-ble and comparable levels. These criteria resulted in the selec-tion of six large facilities in the New York area and furtherextraction ofassessments that met the additional inclusion crite-ria as noted above. All facility and resident identifying informa-tion were stripped prior to dataset delivery to this researcher.The data that is being used in this study includes the MDS datafiles from 1993-1995, which included 8,103 residents for a totalof 44,733 individual assessments. Each assessment has 772variables. The MDS file is part of the RCFA Multi-State Nurs-ing Home Case-Mix and Quality (NHCMQ) demonstrationproject. The HCFA Inpatient Claims Datafile, which contains8,524 claims with 51 variables each (matching the residents andassessments included in the MDS file), was extracted and willbe used for classifier training. Twenty-eight OSCAR certifica-tion surveys specific to the selected facilities (272 fields per sur-vey) will be used for the "facility factor" variables.The inclusion of the Inpatient Claims File is based upon theneed for the outcome of "admission" to validate the classifica-tion scheme derived from the data mining experiment. Presenceof inpatient claims data can be said to represent an acute careadmission. The Inpatient Claims File includes artificially con-structed patient identifiers for matching purposes, admissionand discharge dates, and diagnosis codes. This file will be usedto validate the results of the classifier. The variables to be usedas classifier input will require reduction via a Principle Compo-nents Analysis. Several data mining algorithmic approachessuch as association, clustering, classification, sequence-basedanalysis, and estimation, can be used to extract the relevant rela-tionships in the data. The most common approach (and that cho-sen for this study) is classification algorithms, based on neuralnetworks.The classification being utilized involves using a set ofpre-clas-sified examples to develop a model, which can then be used toclassify (via a standard back propagation neural network) otherdata in the same domain. In this case, the training, testing, andimplementation of the classifier will involve subsetting a frac-tion ofthe MDS dataset and running the neural net on a trainingsubset comprised'of pre-classified cases (those who have beenadmitted to an acute care facility). The neural net will then beused to determine the set of parameters and weights required toproperly discriminate the outcome of "admission". The per-formance of the classifier will then evaluated by comparing thescheme results to actual outcomes of "admission" containedwithin the data set. Implementation of the classifier in the pre-dictive mode will then used to classify the remaining cases(those without the claims file attached).The importance of this technique is that the classifier, if vali-dated on this particular standardized health dataset, can beimplemented in the predictive mode to support decision-makingactivities in similar 'LTC settings. This can have importantimplications for the design of automated MDS systems forLTC. Immediate feedback in the form of alerts to cliniciansabout the risk ofadmission to an acute care facility based on theinput to the MDS may alter the plan ofcare. This in tum has thepotential to lead to improved patient outcomes and a reductionin cost shifting from LTC to acute care facilities.ConclusionThis design and preliminary study description paper presentswork which is currently underway as part ofa doctoral disserta-tion. The intent ofthis study is to investigate and test the poten-tial ofthe MDS assessment as a tool for predicting admission toacute care from long-term care facilities. The use ofKDD andclassification algorithms were presented and described as waysto perform this study. The process ofset extraction and cleaningis underway, which has resulted in a dataset comprised of44,733 assessments. The development and testing of the classi-fier on this large MDS file is anticipated in the near future.References[1] Fayyad, U. Data Mining and Knowledge Discovery: Mak-ing Sense Out of Data. IEEE Expert, Oct. 1996: 20-25.[2] Fayyad, D., Piatetesky-Shapiro, G., Smyth, P. From DataMining to Knowledge Discovery. In Fayyad. D.,Piatetesky-Shapiro, G., Smyth, P (Eds) Advances inKnowledge Discovery and Data Mining. MIT Press,Cambridge, MA; 1996: 1-36.[3] Goodwin, L. Data Mining for Improved Patient Outcomes.In press; Connections, 1997.[4] Kushniruk, A., Patel, V., Fleizer, D. Analysis of MedicalDecision Making: A Cognitive Perspective on MedicalInformatics. In; Proceedings of the Nineteenth AnnualSymposium on Computer Applications in Medical Care.Hanley & Belfus: 1995, 193-197.[5] Phillips, C., Hawes, C., Mor, V., Fries, B., & Morris, J.1321A. AbbottEvaluation of the Nursing Home Resident AssessmentInstrument; Executive Summary. HCFA Contract #88-500-0055. 1996.[6] http:/www.rti.orgfpublicationsIRAI_gerontologist.html[7] Duxbury, (1996). Geriatrics:Unmasking polypharmacyproblems and adverse drug effects. Consultant, April,pp.762-776.[8] Charleson, M. (1987). Morbidity during hospitalization:Can we predict it? Journal ofChronic Disease, (40), 705.[9] Gillick, M. (1982). Adverse consequences of hospitaliza-tion in the elderly. Society of Science in Medicine, (16),1033.Address for correspondencePatricia A. Abbott, MS, RNCUniversity ofMaryland School of Nursing655 West Lombard StreetDepartment of EAHPIBaltimore, MD 21201 USAabbott@gl.umbc.edu

Can the US Minimum Data Set Be Used for Predicting Admissions to Acute Care Facilities?

https://epublications.marquette.edu/cgi/viewcontent.cgi?article=1240&amp;context=mgmt_fac

Can the US Minimum Data Set Be Used for Predicting Admissions to Acute Care Facilities?

Abstract

Similar works

Full text

Available Versions

epublications@Marquette