Techniques for defining geographic boundaries for health regions

Abstract

Many federal and state programs require the geographic partitioning of states into regions for health services planning, monitoring, and/or administration. A common consideration for such programs is that region boundaries should be drawn so as to maximize the proportion of the state's population that receives health care services in its region of residence. Defining region boundaries thus may be viewed as a problem of partitioning a set of N small area1 units (e.g. counties) into M subsets (regions) so as to minimize interactions (patient flow) among subsets. This paper describes three algorithms for region design and compares them in terms of computerprocessing efficiency and solution value based on results from a number of test cases. Application of two of the algorithms, one based on the greedy heuristic and the other incorporating a max-flowlmin-cut procedure, to a problem of dividing a metropolitan region into separate service areas for clusters of hospitals is also described.

Logistics Chain Optimization and Scheduling of Hospital Pharmacy Drugs Using Genetic Algorithms

“…Clusteringhasbeenusedtosolveproblemsindiversefieldssuchasbioinformatics,anddatamining. WilliamThomashascontributedinclusteringhospitalwithclusteringcommunitiesandhospitals simultaneously,whichledtoageographicallyconnectedareaofpopulationsservedbyacommon cluster of hospitals (Griffith, 1972;Thomas, 1979;Thomas et al, 1981), the approach has been appliedbyStateofMichigan.Z.Obradovic(Albarakati&Obradovic,2017)proposedanapproach basedondisease-specifichospitalnetworkswithconsideringsimilarityamongsymptoms.k-mean isamongthemostpopularclusteringalgorithms (Jain,2013),K-meansisanunsupervisedalgorithm fornon-hierarchicalclustering,makingitpossibletogroupinKdistinctclusterstheobservationsof thedataset.Thus,similardatawillbeinthesamecluster.PaulL(Delamateretal.,2013)audited similarityingeographiclocationandpatientutilizationtocreatehealthcarefacilitiesclustersbyusing k-meansclustering.RuthLavergneM (Lavergne,2016)usedclusteranalysistogrouphealthcare facilitiesbasedonthedistributionofhealthcarespendingacrossservicecategorieswithk-meansas aclusteringtechnique. Ontheotherhand,XiuguoChen(Chenetal.,2009)usedWeightedk-Means AlgorithmtoclusterText,DrissandAbdellah(Serrou&Abouabdellah,2016showedthatinthe caseofcentralizedpharmacies,thecostoftransportisreduced,whichmakesthelogisticschainless expensive.Manystudieshavebeenproposedtosolvetheroutingandschedulingproblem,in1995 SumarchrastandMarkham(Nikbakhsh&Zegordi,2010)introducedtheMDVRP.WilliamHo (Ho etal.,2008)proposedtwosolutionsfortheMDVRPproblembasedonGeneticalgorithm.…”

Designing efficient and balanced police patrol districts on an urban street network

“…Depending on the context, districting is also called territory design, territory alignment, zone design, sector design, spatially constrained clustering, or regionalisation. Districting problems have been motivated by and applied to a large number of fields, ranging from political districting of electoral areas (Vickrey 1961, Cirincione et al 2000, Bozkaya et al 2003, sales and service territory alignment (Hess and Samuels 1971, Blais et al 2003, Galvão et al 2006, health care districting (Thomas 1979), school districting (Clarke and Surkis 1968, Schoepfle and Church 1991, Caro et al 2004, to police patrol districting (Mitchell 1972, Curtin et al 2005.…”

Optimal consolidation of municipalities: An analysis of alternative designs

“…For example, in political redistricting a typical objective is to ensure that district sizes (populations) do not differ by more than a certain amount, and each district consists of a compact contiguous area. In defining health care regions, the objective may be to maximize the proportion of the state's population that receives health care in its region of residence [25]. The municipality consolidation problem, on the other hand, is usually driven by economies of scale in the provision of public services.…”

“…Optimal consolidation is closely related to optimal political redistricting [8,10,17], to defining geographic boundaries for health regions [25], to allocating and utilizing park and recreation 104 Malachy Carey et al…”