Impact of a Patient Navigator Program on Hospital-Based and Outpatient Utilization Over 180 Days in a Safety-Net Health System
Background With emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies. Objective To determine the effect of a care transition program using patient navigators (PNs) on health service utilization among high-risk safety-net patients ov...
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| Vydáno v: | Journal of general internal medicine : JGIM Ročník 32; číslo 9; s. 981 - 989 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer US
01.09.2017
Springer Nature B.V |
| Témata: | |
| ISSN: | 0884-8734, 1525-1497, 1525-1497 |
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| Abstract | Background
With emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies.
Objective
To determine the effect of a care transition program using patient navigators (PNs) on health service utilization among high-risk safety-net patients over a 180-day period.
Design
Randomized controlled trial conducted October 2011 through April 2013.
Participants
Patients admitted to the general medicine service with ≥1 readmission risk factor: (1) age ≥ 60; (2) in-network inpatient admission within prior 6 months; (3) index length of stay ≥ 3 days; or (4) admission diagnosis of heart failure or (5) chronic obstructive pulmonary disease. The analytic sample included 739 intervention patients, 1182 controls.
Interventions
Through hospital visits and 30 days of post-discharge telephone outreach, PNs provided coaching and assistance with medications, appointments, transportation, communication with primary care, and self-care.
Main Measures
Primary outcomes: (1) hospital-based utilization, a composite of ED visits and hospital admissions; (2) hospital admissions; (3) ED visits; and (4) outpatient visits. We evaluated outcomes following an index discharge, stratified by patient age (≥ 60 and < 60 years), using a 180-day time frame divided into six 30-day periods.
Key Results
The PN program produced starkly different outcomes by patient age. Among older PN patients, hospital-based utilization was consistently
lower
than controls, producing an 18.7% cumulative decrease at 180 days (
p
= 0.038); outpatient visits increased in the critical first 30-day period (
p
= 0.006). Among younger PN patients, hospital-based utilization was 31.7% (
p
= 0.038)
higher
at 180 days, largely reflecting sharply higher utilization in the initial 30 days (
p
= 0.002), with non-significant changes thereafter; outpatient visits experienced no significant changes.
Conclusions
A PN program serving high-risk safety-net patients differentially impacted patients based on age, and among younger patients, outcomes varied over time. Our findings highlight the importance for future research to evaluate care transition programs among different subpopulations and over longer time periods. |
|---|---|
| AbstractList | With emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies.BACKGROUNDWith emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies.To determine the effect of a care transition program using patient navigators (PNs) on health service utilization among high-risk safety-net patients over a 180-day period.OBJECTIVETo determine the effect of a care transition program using patient navigators (PNs) on health service utilization among high-risk safety-net patients over a 180-day period.Randomized controlled trial conducted October 2011 through April 2013.DESIGNRandomized controlled trial conducted October 2011 through April 2013.Patients admitted to the general medicine service with ≥1 readmission risk factor: (1) age ≥ 60; (2) in-network inpatient admission within prior 6 months; (3) index length of stay ≥ 3 days; or (4) admission diagnosis of heart failure or (5) chronic obstructive pulmonary disease. The analytic sample included 739 intervention patients, 1182 controls.PARTICIPANTSPatients admitted to the general medicine service with ≥1 readmission risk factor: (1) age ≥ 60; (2) in-network inpatient admission within prior 6 months; (3) index length of stay ≥ 3 days; or (4) admission diagnosis of heart failure or (5) chronic obstructive pulmonary disease. The analytic sample included 739 intervention patients, 1182 controls.Through hospital visits and 30 days of post-discharge telephone outreach, PNs provided coaching and assistance with medications, appointments, transportation, communication with primary care, and self-care.INTERVENTIONSThrough hospital visits and 30 days of post-discharge telephone outreach, PNs provided coaching and assistance with medications, appointments, transportation, communication with primary care, and self-care.Primary outcomes: (1) hospital-based utilization, a composite of ED visits and hospital admissions; (2) hospital admissions; (3) ED visits; and (4) outpatient visits. We evaluated outcomes following an index discharge, stratified by patient age (≥ 60 and < 60 years), using a 180-day time frame divided into six 30-day periods.MAIN MEASURESPrimary outcomes: (1) hospital-based utilization, a composite of ED visits and hospital admissions; (2) hospital admissions; (3) ED visits; and (4) outpatient visits. We evaluated outcomes following an index discharge, stratified by patient age (≥ 60 and < 60 years), using a 180-day time frame divided into six 30-day periods.The PN program produced starkly different outcomes by patient age. Among older PN patients, hospital-based utilization was consistently lower than controls, producing an 18.7% cumulative decrease at 180 days (p = 0.038); outpatient visits increased in the critical first 30-day period (p = 0.006). Among younger PN patients, hospital-based utilization was 31.7% (p = 0.038) higher at 180 days, largely reflecting sharply higher utilization in the initial 30 days (p = 0.002), with non-significant changes thereafter; outpatient visits experienced no significant changes.KEY RESULTSThe PN program produced starkly different outcomes by patient age. Among older PN patients, hospital-based utilization was consistently lower than controls, producing an 18.7% cumulative decrease at 180 days (p = 0.038); outpatient visits increased in the critical first 30-day period (p = 0.006). Among younger PN patients, hospital-based utilization was 31.7% (p = 0.038) higher at 180 days, largely reflecting sharply higher utilization in the initial 30 days (p = 0.002), with non-significant changes thereafter; outpatient visits experienced no significant changes.A PN program serving high-risk safety-net patients differentially impacted patients based on age, and among younger patients, outcomes varied over time. Our findings highlight the importance for future research to evaluate care transition programs among different subpopulations and over longer time periods.CONCLUSIONSA PN program serving high-risk safety-net patients differentially impacted patients based on age, and among younger patients, outcomes varied over time. Our findings highlight the importance for future research to evaluate care transition programs among different subpopulations and over longer time periods. Background With emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies. Objective To determine the effect of a care transition program using patient navigators (PNs) on health service utilization among high-risk safety-net patients over a 180-day period. Design Randomized controlled trial conducted October 2011 through April 2013. Participants Patients admitted to the general medicine service with ≥1 readmission risk factor: (1) age ≥ 60; (2) in-network inpatient admission within prior 6 months; (3) index length of stay ≥ 3 days; or (4) admission diagnosis of heart failure or (5) chronic obstructive pulmonary disease. The analytic sample included 739 intervention patients, 1182 controls. Interventions Through hospital visits and 30 days of post-discharge telephone outreach, PNs provided coaching and assistance with medications, appointments, transportation, communication with primary care, and self-care. Main Measures Primary outcomes: (1) hospital-based utilization, a composite of ED visits and hospital admissions; (2) hospital admissions; (3) ED visits; and (4) outpatient visits. We evaluated outcomes following an index discharge, stratified by patient age (≥ 60 and < 60 years), using a 180-day time frame divided into six 30-day periods. Key Results The PN program produced starkly different outcomes by patient age. Among older PN patients, hospital-based utilization was consistently lower than controls, producing an 18.7% cumulative decrease at 180 days ( p = 0.038); outpatient visits increased in the critical first 30-day period ( p = 0.006). Among younger PN patients, hospital-based utilization was 31.7% ( p = 0.038) higher at 180 days, largely reflecting sharply higher utilization in the initial 30 days ( p = 0.002), with non-significant changes thereafter; outpatient visits experienced no significant changes. Conclusions A PN program serving high-risk safety-net patients differentially impacted patients based on age, and among younger patients, outcomes varied over time. Our findings highlight the importance for future research to evaluate care transition programs among different subpopulations and over longer time periods. With emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies. To determine the effect of a care transition program using patient navigators (PNs) on health service utilization among high-risk safety-net patients over a 180-day period. Randomized controlled trial conducted October 2011 through April 2013. Patients admitted to the general medicine service with ≥1 readmission risk factor: (1) age ≥ 60; (2) in-network inpatient admission within prior 6 months; (3) index length of stay ≥ 3 days; or (4) admission diagnosis of heart failure or (5) chronic obstructive pulmonary disease. The analytic sample included 739 intervention patients, 1182 controls. Through hospital visits and 30 days of post-discharge telephone outreach, PNs provided coaching and assistance with medications, appointments, transportation, communication with primary care, and self-care. Primary outcomes: (1) hospital-based utilization, a composite of ED visits and hospital admissions; (2) hospital admissions; (3) ED visits; and (4) outpatient visits. We evaluated outcomes following an index discharge, stratified by patient age (≥ 60 and < 60 years), using a 180-day time frame divided into six 30-day periods. The PN program produced starkly different outcomes by patient age. Among older PN patients, hospital-based utilization was consistently lower than controls, producing an 18.7% cumulative decrease at 180 days (p = 0.038); outpatient visits increased in the critical first 30-day period (p = 0.006). Among younger PN patients, hospital-based utilization was 31.7% (p = 0.038) higher at 180 days, largely reflecting sharply higher utilization in the initial 30 days (p = 0.002), with non-significant changes thereafter; outpatient visits experienced no significant changes. A PN program serving high-risk safety-net patients differentially impacted patients based on age, and among younger patients, outcomes varied over time. Our findings highlight the importance for future research to evaluate care transition programs among different subpopulations and over longer time periods. BackgroundWith emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies.ObjectiveTo determine the effect of a care transition program using patient navigators (PNs) on health service utilization among high-risk safety-net patients over a 180-day period.DesignRandomized controlled trial conducted October 2011 through April 2013.ParticipantsPatients admitted to the general medicine service with ≥1 readmission risk factor: (1) age ≥ 60; (2) in-network inpatient admission within prior 6 months; (3) index length of stay ≥ 3 days; or (4) admission diagnosis of heart failure or (5) chronic obstructive pulmonary disease. The analytic sample included 739 intervention patients, 1182 controls.InterventionsThrough hospital visits and 30 days of post-discharge telephone outreach, PNs provided coaching and assistance with medications, appointments, transportation, communication with primary care, and self-care.Main MeasuresPrimary outcomes: (1) hospital-based utilization, a composite of ED visits and hospital admissions; (2) hospital admissions; (3) ED visits; and (4) outpatient visits. We evaluated outcomes following an index discharge, stratified by patient age (≥ 60 and < 60 years), using a 180-day time frame divided into six 30-day periods.Key ResultsThe PN program produced starkly different outcomes by patient age. Among older PN patients, hospital-based utilization was consistently lower than controls, producing an 18.7% cumulative decrease at 180 days (p = 0.038); outpatient visits increased in the critical first 30-day period (p = 0.006). Among younger PN patients, hospital-based utilization was 31.7% (p = 0.038) higher at 180 days, largely reflecting sharply higher utilization in the initial 30 days (p = 0.002), with non-significant changes thereafter; outpatient visits experienced no significant changes.ConclusionsA PN program serving high-risk safety-net patients differentially impacted patients based on age, and among younger patients, outcomes varied over time. Our findings highlight the importance for future research to evaluate care transition programs among different subpopulations and over longer time periods. |
| Author | Balaban, Richard B. Burns, Marguerite E. Vialle-Valentin, Catherine E. Larochelle, Marc R. Zhang, Fang Galbraith, Alison A. Ross-Degnan, Dennis |
| Author_xml | – sequence: 1 givenname: Richard B. surname: Balaban fullname: Balaban, Richard B. email: rbalaban@challiance.org organization: Somerville Hospital Primary Care, Cambridge Health Alliance, Harvard Medical School – sequence: 2 givenname: Fang surname: Zhang fullname: Zhang, Fang organization: Harvard Medical School, Harvard Pilgrim Health Care Institute – sequence: 3 givenname: Catherine E. surname: Vialle-Valentin fullname: Vialle-Valentin, Catherine E. organization: Harvard Medical School, Harvard Pilgrim Health Care Institute – sequence: 4 givenname: Alison A. surname: Galbraith fullname: Galbraith, Alison A. organization: Harvard Medical School, Harvard Pilgrim Health Care Institute – sequence: 5 givenname: Marguerite E. surname: Burns fullname: Burns, Marguerite E. organization: University of Wisconsin – sequence: 6 givenname: Marc R. surname: Larochelle fullname: Larochelle, Marc R. organization: Boston University School of Medicine – sequence: 7 givenname: Dennis surname: Ross-Degnan fullname: Ross-Degnan, Dennis organization: Harvard Medical School, Harvard Pilgrim Health Care Institute |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28523476$$D View this record in MEDLINE/PubMed |
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| Copyright | Society of General Internal Medicine 2017 Journal of General Internal Medicine is a copyright of Springer, (2017). All Rights Reserved. |
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| References | KansagaraDChiovaroJCKagenDSo many options, where do we start? An overview of the care transitions literatureJ Hosp Med201611322123010.1002/jhm.250226551918 BalabanRGalbraithABurnsMVialle-ValentinCLarochelleMRoss-DegnanDA patient navigator intervention to reduce hospital readmissions among high-risk safety-net patients: a randomized controlled trialJ Gen Intern Med201530790791510.1007/s11606-015-3185-x256171664471016 EinstadterDCebulRDFrantaPREffect of a nurse case manager on postdischarge follow-upJ Gen Intern Med199611116846881:STN:280:DyaK2s7hslWltQ%3D%3D10.1007/BF026001609120655 National Quality Forum. Measuring Performance. 2017. http://www.qualityforum.org/Measuring_Performance/Measuring_Performance.aspx. Accessed Jan 10, 2017. AmarasinghamRMooreBJTabakYPAn automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record dataMed Care2010481198198810.1097/MLR.0b013e3181ef60d920940649 LiangK-YZegerSLongitudinal data analysis using generalized linear modelsBiometrika198673132210.1093/biomet/73.1.13 Corder GW, Foreman DI. Nonparametric Statistics: An Introduction. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach. New York: John Wiley & Sons; 2009:1–11. AshtonCMDel JuncoDJSouchekJWrayNPMansyurCLThe association between the quality of inpatient care and early readmission: a meta-analysis of the evidenceMed Care19973510104410591:STN:280:DyaK2svnvVagtw%3D%3D10.1097/00005650-199710000-00006 WeissmanJSSternRSEpsteinAMThe impact of patient socioeconomic status and other social factors on readmission: a prospective study in four Massachusetts hospitalsInquiry19943121631721:STN:280:DyaK2c3pt1amtQ%3D%3D TangNSteinJHsiaRYMaselliJHGonzalesRTrends and characteristics of US emergency department visits, 1997-2007JAMA201030466646701:CAS:528:DC%2BC3cXhtVelt7rK10.1001/jama.2010.11123123697 KindAJBartelsCMellMWMullahyJSmithMFor-profit hospital status and rehospitalizations at different hospitals: an analysis of Medicare dataAnn Intern Med20101531171872710.7326/0003-4819-153-11-201012070-00005211352953058683 BenbassatJTaraginMHospital readmissions as a measure of quality of health care: advantages and limitationsArch Intern Med20001608107410811:STN:280:DC%2BD3c3kvVClsw%3D%3D10.1001/archinte.160.8.107410789599 MudgeADenaroCScottIBennettCHickeyAJonesMAThe paradox of readmission: effect of a quality improvement program in hospitalized patients with heart failureJ Hosp Med20105314815310.1002/jhm.56320235283 HasanOMeltzerDOShaykevichSAHospital readmission in general medicine patients: a prediction modelJ Gen Intern Med201025321121910.1007/s11606-009-1196-1 KangoviSBargFKCarterTLongJAShannonRGrandeDUnderstanding why patients of low socioeconomic status prefer hospitals over ambulatory careHealth Affairs20133271196120310.1377/hlthaff.2012.082523836734 BaickerKTaubmanSLAllenHLThe Oregon experiment—effects of Medicaid on clinical outcomesN Engl J Med201336818171317221:CAS:528:DC%2BC3sXnt1WltL4%3D10.1056/NEJMsa1212321236350513701298 Hospital Compare. 2016. https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/hospitalqualityinits/hospitalcompare.html. 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PeikesDChenASchoreJBrownREffects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries: 15 randomized trialsJAMA200930166036181:CAS:528:DC%2BD1MXhvVOqur0%3D10.1001/jama.2009.12619211468 LeppinALGionfriddoMRKesslerMPreventing 30-day hospital readmissions: a systematic review and meta-analysis of randomized trialsJAMA Intern Med201417471095110710.1001/jamainternmed.2014.16084249925 FinkelsteinATaubmanSWrightBThe Oregon Health Insurance Experiment: Evidence from the First YearQ J Econ201212731057112610.1093/qje/qjs0203535298 TracyKBurtonMNichCRounsavilleBUtilizing peer mentorship to engage high recidivism substance-abusing patients in treatmentAm J Drug Alcohol Abuse201137652553110.3109/00952990.2011.60038521851202 ByrneMMurphyAWPlunkettPKMcGeeHMMurrayABuryGFrequent attenders to an emergency department: a study of primary health care use, medical profile, and psychosocial characteristicsAnn Emerg Med200341330931810.1067/mem.2003.6812605196 WeinbergerMOddoneEZHendersonWGDoes increased access to primary care reduce hospital readmissions? 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JencksSFWilliamsMVColemanEARehospitalizations among patients in the Medicare fee-for-service programN Engl J Med200936014141814281:CAS:528:DC%2BD1MXjvFGju7c%3D10.1056/NEJMsa080356319339721 BillingsJDixonJMijanovichTWennbergDCase finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patientsBMJ2006333756332710.1136/bmj.38870.657917.AE168158821539047 Center for Health Information and Informatics. Hospital-Wide Adult All-Payer Readmissions in Massachusetts: 2011–2013. 2015. http://www.chiamass.gov/assets/docs/r/pubs/15/CHIA-Readmissions-Report-June-2015.pdf. Accessed April 13, 2017. HansenLOYoungRSHinamiKLeungAWilliamsMVInterventions to reduce 30-day rehospitalization: a systematic reviewAnn Intern Med2011155852052810.7326/0003-4819-155-8-201110180-00008 Tobias C, Levinson J, Simon L. Reinventing the HMO: The Next Generation of Medicaid Managed Care. Center for Health Care Strategies, Inc. 2005. http://www.chcs.org/resource/reinventing-the-hmo-medicaid-managed-care-for-members-with-complex-needs/. Accessed April 13,2017. McCullaghPNelderJGeneralized Linear Models19892Boca RatonChapman & Hall10.1007/978-1-4899-3242-6 P McCullagh (4074_CR19) 1989 D Peikes (4074_CR20) 2009; 301 SF Jencks (4074_CR13) 2009; 360 LO Hansen (4074_CR5) 2011; 155 4074_CR9 AL Leppin (4074_CR6) 2014; 174 R Balaban (4074_CR10) 2015; 30 A Mudge (4074_CR24) 2010; 5 K Baicker (4074_CR31) 2013; 368 L Horwitz (4074_CR8) 2011; 2011 4074_CR16 4074_CR17 M Byrne (4074_CR28) 2003; 41 CM Ashton (4074_CR4) 1997; 35 DM Smith (4074_CR11) 2000; 53 N Tang (4074_CR29) 2010; 304 D Kansagara (4074_CR7) 2016; 11 S Kangovi (4074_CR30) 2013; 32 AJ Kind (4074_CR32) 2010; 153 J Benbassat (4074_CR3) 2000; 160 M Weinberger (4074_CR22) 1996; 334 A Finkelstein (4074_CR26) 2012; 127 K Tracy (4074_CR21) 2011; 37 JS Weissman (4074_CR27) 1994; 31 4074_CR25 4074_CR2 4074_CR1 O Hasan (4074_CR15) 2010; 25 J Billings (4074_CR12) 2006; 333 D Einstadter (4074_CR23) 1996; 11 K-Y Liang (4074_CR18) 1986; 73 R Amarasingham (4074_CR14) 2010; 48 28634904 - J Gen Intern Med. 2017 Sep;32(9):1025 |
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Veterans Affairs Cooperative Study Group on Primary Care and Hospital ReadmissionN Engl J Med199633422144114471:STN:280:DyaK283hvVyhuw%3D%3D10.1056/NEJM1996053033422068618584 – reference: Corder GW, Foreman DI. Nonparametric Statistics: An Introduction. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach. New York: John Wiley & Sons; 2009:1–11. – reference: Tobias C, Levinson J, Simon L. Reinventing the HMO: The Next Generation of Medicaid Managed Care. Center for Health Care Strategies, Inc. 2005. http://www.chcs.org/resource/reinventing-the-hmo-medicaid-managed-care-for-members-with-complex-needs/. 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An overview of the care transitions literatureJ Hosp Med201611322123010.1002/jhm.250226551918 – reference: LeppinALGionfriddoMRKesslerMPreventing 30-day hospital readmissions: a systematic review and meta-analysis of randomized trialsJAMA Intern Med201417471095110710.1001/jamainternmed.2014.16084249925 – reference: TracyKBurtonMNichCRounsavilleBUtilizing peer mentorship to engage high recidivism substance-abusing patients in treatmentAm J Drug Alcohol Abuse201137652553110.3109/00952990.2011.60038521851202 – reference: BillingsJDixonJMijanovichTWennbergDCase finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patientsBMJ2006333756332710.1136/bmj.38870.657917.AE168158821539047 – reference: EinstadterDCebulRDFrantaPREffect of a nurse case manager on postdischarge follow-upJ Gen Intern Med199611116846881:STN:280:DyaK2s7hslWltQ%3D%3D10.1007/BF026001609120655 – reference: HasanOMeltzerDOShaykevichSAHospital readmission in general medicine patients: a prediction modelJ Gen Intern Med201025321121910.1007/s11606-009-1196-1 – reference: Center for Health Information and Informatics. Hospital-Wide Adult All-Payer Readmissions in Massachusetts: 2011–2013. 2015. http://www.chiamass.gov/assets/docs/r/pubs/15/CHIA-Readmissions-Report-June-2015.pdf. Accessed April 13, 2017. – reference: McCullaghPNelderJGeneralized Linear Models19892Boca RatonChapman & Hall10.1007/978-1-4899-3242-6 – reference: JencksSFWilliamsMVColemanEARehospitalizations among patients in the Medicare fee-for-service programN Engl J Med200936014141814281:CAS:528:DC%2BD1MXjvFGju7c%3D10.1056/NEJMsa080356319339721 – reference: Diggle PHP, Liang K-Y, Zeger S. Analysis of Longitudinal Data. Oxford: Oxford University Press; 2013. – reference: SmithDMGiobbie-HurderAWeinbergerMPredicting non-elective hospital readmissions: a multi-site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and ReadmissionsJ Clin Epidemiol20005311111311181:STN:280:DC%2BD3M%2FltV2itQ%3D%3D10.1016/S0895-4356(00)00236-511106884 – reference: LiangK-YZegerSLongitudinal data analysis using generalized linear modelsBiometrika198673132210.1093/biomet/73.1.13 – reference: BalabanRGalbraithABurnsMVialle-ValentinCLarochelleMRoss-DegnanDA patient navigator intervention to reduce hospital readmissions among high-risk safety-net patients: a randomized controlled trialJ Gen Intern Med201530790791510.1007/s11606-015-3185-x256171664471016 – reference: KindAJBartelsCMellMWMullahyJSmithMFor-profit hospital status and rehospitalizations at different hospitals: an analysis of Medicare dataAnn Intern Med20101531171872710.7326/0003-4819-153-11-201012070-00005211352953058683 – reference: BaickerKTaubmanSLAllenHLThe Oregon experiment—effects of Medicaid on clinical outcomesN Engl J Med201336818171317221:CAS:528:DC%2BC3sXnt1WltL4%3D10.1056/NEJMsa1212321236350513701298 – reference: National Quality Forum. 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With emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies.
Objective
To... With emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies. To determine the effect of a... BackgroundWith emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies.ObjectiveTo determine... With emerging global payment structures, medical systems need to understand longer-term impacts of care transition strategies.BACKGROUNDWith emerging global... |
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| SubjectTerms | Adult Age Age Factors Aged Ambulatory Care - statistics & numerical data Chronic obstructive pulmonary disease Continuity of care Delivery of Health Care, Integrated Disease control Emergency Service, Hospital - statistics & numerical data Female Health care Health services Heart Heart diseases Heart Failure - epidemiology Hospitals Humans Internal Medicine Length of Stay - statistics & numerical data Lung diseases Male Medicine Medicine & Public Health Middle Aged Navigators Obstructive lung disease Original Research Outcome and Process Assessment (Health Care) Patient Admission - statistics & numerical data Patient admissions Patient Navigation - statistics & numerical data Patient Readmission - statistics & numerical data Patient Transfer - organization & administration Patient Transfer - standards Patients Program Evaluation Pulmonary Disease, Chronic Obstructive - epidemiology Risk Factors Safety Subpopulations Time Factors Utilization |
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| Title | Impact of a Patient Navigator Program on Hospital-Based and Outpatient Utilization Over 180 Days in a Safety-Net Health System |
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