The Impact of Medicaid Expansions on Nonprofit Hospitals

Wang, Rui; AbouAssi, Khaldoun

doi:10.1515/npf-2020-0053

Cited by 5 publications

(2 citation statements)

References 72 publications

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“…IRS Form 990 data, the major data resource for nonprofit research and the only systematic yearly government data source on nonprofit organizations, are limited by their content, and widely useable data are typically only released two to three years after being collected (Fyall, Moore, and Gugerty 2018;Kim and Charles 2016). This lack of representative and timely data hinders our efforts to understand the composition and health of the sector and how changes in public policy and economic conditions affect nonprofits' activities (Besel, Williams, and Klak 2011;Twombly 2003;Wang and AbouAssi 2021).…”

Section: Introductionmentioning

confidence: 99%

Selling God: Using Big Data to Target Vulnerable Individuals

Geiszler,

Hajjat,

McKinley

2023

asr

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Over five decades ago, professors of marketing Philip Kotler and Sidney Levy advocated for expanding the use of marketing tools to benefit the nonprofit world. Today, nonprofits are extending this strategy by using big data analytics. Typically controversial in corporate America, nonprofits are using powerful technology to target vulnerable individuals and offer them services. This paper seeks to encourage conversation around this strategy and calls for research that investigates three main areas: the question of ethicality, the question of consequences, and the question of regulation. Without question big data and predictive analytics can be very helpful tools. Similarly, nonprofit organizations can do wonderful things for their communities. However, the intersection of these two elements may have major adverse effects on consumer well-being. The risks at this intersection provide the motivation for this paper.

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Section: Introductionmentioning

confidence: 99%

Selling God: Using Big Data to Target Vulnerable Individuals

Geiszler,

Hajjat,

McKinley

2023

asr

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“…Much of the research that investigates the quality of CHNAs utilizes the publicly available reports provided by each hospital that does them 3,4,[7][8][9] and analyzes them at the state level. Other research that examines CHNAs at a national level focuses on its financial ramifications rather than the assessment process and extracts data from the IRS Form 990 Schedule H. 1,5,6,[10][11][12][13][14][15][16] This suggests that there is a gap in examining the quality of assessments at a national level. There is one study to date that conducted a content analysis on a national level.…”

mentioning

confidence: 99%

One Decade Later: The Generalizability, Diversity, and Inclusion of Community Health Needs Assessments

Summers-Gabr

Cantrall

2022

Journal of Public Health Management and Practice

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Context: The 2010 Patient Protection and Affordable Care Act aimed to reduce health disparities and change medicine to be more community-driven. To maintain tax-exempt status, hospitals must complete a Community Health Needs Assessment (CHNA) every 3 years. This assessment must (a) integrate input from individuals the community serves, (b) make the report publicly assessable, and (c) adopt an implementation strategy based on community health needs identified in the assessment. However, there is little information on how representative CHNAs are of the community. Design: A content analysis was performed on a random sample of CHNA reports. Setting: This investigation examined nonprofit hospitals across the United States. Objectives: This investigation analyzed the quality of CHNAs and described existing CHNA practices through 4 means: (1) identified the type of data included; (2) examined the frequency in the methods of data collection; (3) understood how representative those data are of the hospital's service region; and (4) explored to what extent the hospital addressed diversity and inclusion such as through recruitment. Methods: A stratified random sample was drawn of CHNAs published in the past 3 years (n = 450 reports). The sample was stratified by the US Department of Agriculture's Rural-Urban Continuum codes to balance hospital representation from metro and nonmetro areas. Results: A series of dependent t tests revealed that these hospitals' reports represented a significantly more female, White, college-educated, and older population than the service area. In addition, only 3.12% of hospitals collected primary youth data. Finally, results also found that survey recruitment was not inclusive of individuals who did not have Internet access, could not read, or did not speak English fluently.

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Hospital Revenue Forecast Using Multivariate and Univariate Long Short-Term Memories

Luong

Nguyễn

et al. 2021

Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications

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The Impact of Medicaid Expansions on Nonprofit Hospitals

Cited by 5 publications

References 72 publications

Selling God: Using Big Data to Target Vulnerable Individuals

Selling God: Using Big Data to Target Vulnerable Individuals

One Decade Later: The Generalizability, Diversity, and Inclusion of Community Health Needs Assessments

Hospital Revenue Forecast Using Multivariate and Univariate Long Short-Term Memories

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