PMID- 31390034
OWN - NLM
STAT- MEDLINE
DCOM- 20200615
LR  - 20200615
IS  - 2574-3805 (Electronic)
IS  - 2574-3805 (Linking)
VI  - 2
IP  - 8
DP  - 2019 Aug 2
TI  - Comparison of 3 Safety-Net Hospital Definitions and Association With Hospital 
      Characteristics.
PG  - e198577
LID - 10.1001/jamanetworkopen.2019.8577 [doi]
LID - e198577
AB  - IMPORTANCE: No consensus exists on how to define safety-net hospitals (SNHs) for 
      research or policy decision-making. Identifying which types of hospitals are 
      classified as SNHs under different definitions is key to assessing policies that 
      affect SNH funding. OBJECTIVE: To examine characteristics of SNHs as classified 
      under 3 common definitions. DESIGN, SETTING, AND PARTICIPANTS: This 
      cross-sectional analysis includes noncritical-access hospitals in the Healthcare 
      Cost and Utilization Project State Inpatient Databases from 47 US states for 
      fiscal year 2015, linked to the Centers for Medicare & Medicaid Services Hospital 
      Cost Reports and to the American Hospital Association Annual Survey. Data were 
      analyzed from March 1 through September 30, 2018. EXPOSURES: Hospital 
      characteristics including organizational characteristics, scope of services 
      provided, and financial attributes. MAIN OUTCOMES AND MEASURES: Definitions of 
      SNH based on Medicaid and Medicare Supplemental Security Income inpatient days 
      historically used to determine Medicare Disproportionate Share Hospital (DSH) 
      payments; Medicaid and uninsured caseload; and uncompensated care costs. For each 
      measure, SNHs were defined as those within the top quartile for each state. 
      RESULTS: The 2066 hospitals in this study were distributed across the Northeast 
      (340 [16.5%]), Midwest (587 [28.4%]), South (790 [38.2%]), and West (349 
      [16.9%]). Concordance between definitions was low; 269 hospitals (13.0%) or fewer 
      were identified as SNHs under any 2 definitions. Uncompensated care captured 
      smaller (200 of 523 [38.2%]) and more rural (65 of 523 [12.4%]) SNHs, whereas DSH 
      index and Medicaid and uncompensated caseload identified SNHs that were larger 
      (264 of 518 [51.0%] and 158 of 487 [32.4%], respectively) and teaching facilities 
      (337 of 518 [65.1%] and 229 of 487 [47.0%], respectively) that provided more 
      essential services than non-SNHs. Uncompensated care also distinguished 
      remarkable financial differences between SNHs and non-SNHs. Under the 
      uncompensated care definition, median (interquartile range [IQR]) bad debt ($27.1 
      [$15.5-$44.3] vs $12.8 [$6.7-$21.6] per $1000 of operating expenses; P < .001) 
      and charity care ($19.9 [$9.3-$34.1] vs $9.1 [$4.0-$18.7] per $1000 of operating 
      expenses) were twice as high and median (IQR) unreimbursed costs ($32.6 
      [$12.4-$55.4] vs $23.6 [$9.0-$42.7] per $1000 of operating expenses; P < .001) 
      were 38% higher for SNHs than for non-SNHs. Safety-net hospitals defined by 
      uncompensated care burden had lower median (IQR) total (4.7% [0%-9.9%] vs 5.8% 
      [1.2%-11.2%]; P = .003) and operating (0.3% [-8.0% to 7.2%] vs 2.3% [-3.9% to 
      8.9%]; P < .001) margins than their non-SNH counterparts, whereas differences 
      between SNH and non-SNH profit margins generally were not statistically 
      significant under the other 2 definitions. CONCLUSIONS AND RELEVANCE: Different 
      SNH definitions identify hospitals with different characteristics and financial 
      conditions. The new DSH formula, which accounts for uncompensated care, may lead 
      to redistributed payments across hospitals. Our results may inform which types of 
      hospitals will experience funding changes as DSH payment policies evolve.
FAU - Popescu, Ioana
AU  - Popescu I
AD  - Division of General Internal Medicine and Health Services Research, David Geffen 
      School of Medicine at UCLA, Los Angeles, California.
AD  - Rand Corporation, Los Angeles, California.
FAU - Fingar, Kathryn R
AU  - Fingar KR
AD  - IBM Watson Health, Sacramento, California.
FAU - Cutler, Eli
AU  - Cutler E
AD  - IBM Watson Health, Sacramento, California.
AD  - currently with Qventus, San Jose, California.
FAU - Guo, Jing
AU  - Guo J
AD  - Agency for Healthcare Research and Quality, Rockville, Maryland.
FAU - Jiang, H Joanna
AU  - Jiang HJ
AD  - Agency for Healthcare Research and Quality, Rockville, Maryland.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, U.S. Gov't, P.H.S.
DEP - 20190802
PL  - United States
TA  - JAMA Netw Open
JT  - JAMA network open
JID - 101729235
SB  - IM
CIN - JAMA Netw Open. 2019 Aug 2;2(8):e198562. PMID: 31390030
MH  - Cross-Sectional Studies
MH  - Hospitals/*classification/*statistics & numerical data
MH  - Humans
MH  - Safety-net Providers/*classification/*organization & administration/*statistics & 
      numerical data
MH  - United States
PMC - PMC6686776
COIS- Conflict of Interest Disclosures: Drs Popescu, Fingar, and Cutler reported 
      receiving funding from the Agency for Healthcare Research and Quality as 
      employees or subcontractors of IBM Watson Health during the conduct of the study. 
      No other disclosures were reported.
EDAT- 2019/08/08 06:00
MHDA- 2020/06/17 06:00
PMCR- 2019/08/07
CRDT- 2019/08/08 06:00
PHST- 2019/08/08 06:00 [entrez]
PHST- 2019/08/08 06:00 [pubmed]
PHST- 2020/06/17 06:00 [medline]
PHST- 2019/08/07 00:00 [pmc-release]
AID - 2747477 [pii]
AID - zoi190341 [pii]
AID - 10.1001/jamanetworkopen.2019.8577 [doi]
PST - epublish
SO  - JAMA Netw Open. 2019 Aug 2;2(8):e198577. doi: 10.1001/jamanetworkopen.2019.8577.