Collaborating for COVID-19: Hospital Health Information Exchange and Public Health Partnership

Abstract

Background:

The coronavirus disease (COVID-19) pandemic highlighted the need for effective communication and information sharing among health care organizations and public health systems (PHSs). Health information exchange (HIE) plays a vital role in improving quality control and efficiency in hospital settings, particularly in underserved areas.

Objective:

This study aimed to investigate the variation of HIE availability among hospitals based on their collaboration with the PHS and affiliation with Accountable Care Organizations (ACOs) in 2020, as well as variation by community social determinants of health.

Methods:

The primary data set used for this study comprised the linked data set of the 2020 American Hospital Association (AHA) Annual Survey and the AHA Information Technology Supplement. The measures used included the hospital's participation in HIE networks, availability of data exchange, and HIE measures during the COVID-19 pandemic, including whether hospitals effectively received electronically transmitted information from outside providers for COVID-19 treatment.

Results:

The sample size of hospitals ranged from 1,316 to 1,436, depending on different outcomes related to HIE questions. Of the hospitals surveyed, ∼67% reported public health collaboration and ACO affiliation, while 7% reported neither. Hospitals without public health collaboration or ACO affiliation were more likely to be located in underserved areas. Compared with hospitals without public health collaboration or ACO affiliation, hospitals with both were 9% more likely to report the availability of electronically transmitted clinical information from outside providers and to participate in local and national HIE networks. Furthermore, these hospitals were 30% (marginal effect [ME] = 0.30, p < 0.001) more likely to report effective receipt of information from outside providers for COVID-19 treatment and 12% (ME = 0.12, p = 0.02) more likely to always/often receive clinical information for COVID-19 treatment electronically.

Conclusions:

Hospital collaboration with the PHS and ACO affiliation are associated with greater availability of electronic health data, particularly during the COVID-19 pandemic.

Introduction

The coronavirus disease (COVID-19) pandemic in 2019 caused significant disruptions to hospitals and those in need of basic health care. To combat an unprecedented challenge to our modern health care systems, many hospitals opted to enhance technology capacity to manage patient surges and protect vulnerable populations from contracting COVID-19 within the hospital setting. COVID-19 underscored the need for improved communication and information sharing between health care organizations and government agencies.

With an emphasis on data interoperability, COVID-19 accelerated the use of health information exchange (HIE) in the general hospital system.¹ In 2020, Sittig and Singh noted that 95% of hospitals used electronic health records (EHRs), which enabled the United States (U.S.) to create better data collection infrastructure.² Additionally, electronic HIE allows health care providers to access and share patients' records digitally, leading to more accurate and efficient real-time disease data.³

HIE uses common infrastructure to securely connect electronic clinical patient data from hospitals, laboratories, and other health care organizations with the goal of enabling information to follow patients, facilitating coordinated, effective, and efficient care.⁴ HIEs are commonly at the state or regional levels. Research published in 2021 identified 89 operational HIEs, half connect to the national eHealth Exchange network, led by the Office of the National Coordinator (ONC) for Health Information Technology (HIT).⁵

The structure of IT is vital to U.S. public health systems (PHSs).⁶ Extensive research has shown that integrating PHSs in health care can enhance health care quality, reduce costs, and mitigate racial and ethnic disparities in health care.^7

–12 Recent studies have emphasized the urgent need to modernize data systems and improve HIE within PHSs. Emanuel et al. highlight the importance of developing a comprehensive, real-time, integrated data infrastructure for public health to respond effectively to future public health threats.¹³

Modern public health infrastructure should enable the real-time electronic collection of detailed disease information; merge it with sociodemographic data and even nontraditional data such as genomic or environmental data; and integrate data from local, state, and national public health units, health care systems, public and commercial laboratories, and academic and research institutions.

We argue that such a system is critical not only for responding to pandemics and managing future public health emergencies but also for improving population health and delivering quality care. The interoperability and availability of timely, comprehensive health information among different health care and PHSs are essential for achieving these goals.

In addition, the COVID-19 pandemic brought the role of Accountable Care Organizations (ACOs) and HIT to the forefront of policymakers' attention. A primary goal of participating in ACOs is to promote data interoperability among physicians and hospitals to increase efficiency, reduce costs, and provide better quality treatments for patients.¹⁴ The Centers for Medicare & Medicaid Services (CMS) remains committed to driving accountable care and are continually improving the ACO model.^15,16

Therefore, now is a critical time to understand how the ACO can be designed to enhance data interoperability, care coordination across PHSs and health care settings, and health equity.

The objective of the study is to examine the variation of HIE availability by hospital collaboration with the PHS and hospital affiliation with ACO, as well as the variation by community social determinants of health during the first year of the COVID-19 pandemic. Our study focuses on hospitals since they are critical health care resources, especially for underserved populations, which makes the hospital setting and HIE infrastructure critically important during the pandemic.

Hospital-HIE infrastructure requires establishing data communication with outside health care providers and will be implemented more efficiently when there are more robust community cross-sectional partnerships. We expect that community partnerships played a pronounced role in population health and well-being during the pandemic. Hence, we hypothesize that hospitals collaborating with PHSs and/or having ACO affiliation would be more likely to enhance data exchanges with external providers (through partnerships), especially during the COVID-19 pandemic.

Our study aims to provide the first empirical evidence of disparities in PHS collaboration and ACO participation in underserved areas, shedding light on the need to promote and implement collaboration and ACO models in these regions.

Methods

DATA

Our main data set comprised the linked data sets of the 2020 American Hospital Association (AHA) Annual Survey and the AHA IT Supplement. The AHA Annual Survey captures measures relating to collaboration between hospitals and PHSs.¹⁷ The AHA IT Supplement primarily gathers data on hospitals' technology indicators such as key domains of interoperability, barriers to interoperability, and patient engagement technology.¹⁸ The newest data in 2020 include information exchange related to COVID-19 and hospital capacity reporting.

Using the hospital county ID, we merged the data with the 2018 Social Vulnerability Index (SVI) provided by the Centers for Disease Control (CDC). The SVI has been used to study topics such as emergency department use due to heat-related outcomes and COVID-19 incidence.^19,20 The sample sizes vary slightly by different outcomes, ranging from n = 1,316 to 1,436.

OUTCOME MEASURES

The study focuses on the following outcomes related to HIE and HIE during the COVID-19 pandemic: hospital's active participation in local HIE or national HIE networks, availability of electronic clinical information from outside providers, whether the hospital had received electronically transmitted information needed to effectively treat COVID-19 from outside providers (strongly agree or agree), and the frequency (always/often) of receiving clinical information necessary for treating patients with COVID-19 from outside providers electronically.

KEY INDEPENDENT VARIABLES

Our key independent variables are the indicators of hospital–public health collaboration. Hospital–public health collaboration equals “1” if hospitals had collaborations with local or state public health departments and other local or state government or social service organizations and “0” otherwise.²¹ We also included an indicator for hospital participation in an ACO.

Finally, we cross-tabulated hospital–public health collaboration and ACO affiliation and created four indicators: no hospital–public health collaboration and no ACO affiliation; hospital–public health collaboration, but no ACO affiliation; no hospital–public health collaboration, but with ACO affiliation; and hospital–public health collaboration and ACO affiliation.

OTHER INDEPENDENT VARIABLES

Our covariates included urban/rural hospital status, teaching hospital status, hospital ownership (for profit, not for profit, or government owned), and hospital number of beds (small: <100, medium: 100–499, and large: ≥500). We also examined HIE variation by SVI quartile (under the 25th percentile, between 25th and 50th percentiles, between 50th and 75th percentiles, and above the 75th percentile).

SVI uses U.S. Census data to identify the vulnerability of populations in geographic areas to hardship following an event or disaster based on social factors such as poverty, crowded housing, and access to transportation.²² A higher score indicates greater vulnerability. Our decomposition results reviewed the SVI below the 75th percentile versus SVI above the 75th percentile.

DATA ANALYSES

We presented summary statistics of hospital-based HIE variables and hospital characteristics by hospital–public health collaboration and the cross-tabulation of public health collaboration and ACO affiliation. First, we ran a simple regression model by only controlling for hospital–public health collaboration and ACO affiliation. Second, for a full model, we applied state fixed-effects multivariable logistic regression to examine the four cross-tabulations of hospital–public health collaboration and ACO affiliation. In this full model, we controlled for the covariates presented above.

Finally, we applied the decomposition model to understand the association between hospital–public health collaboration, ACO, and hospital HIE by SVI. We tested regression by two groups: hospitals located in counties with SVI above the 75th percentile versus below the 75th percentile. In each regression, we controlled for all covariates, including the hospital–public health collaboration and ACO affiliation.

We conducted sensitivity analyses to assess the robustness of our findings. We tested different model specifications with and without state fixed effects, using different sets of hospital characteristics, and with or without the SVI. We also tested different county measures using the Area Health Resource File. Results are similar and available upon request. Our analyses were performed using Stata 17 MP4. This study has received approval from the Institutional Review Board at the University of Maryland.

Results

Figure 1 shows that hospitals with public health collaborations were more likely to report that clinical information was available electronically from outside providers and were more likely to have participated in local and national HIE networks. Over 50% of hospitals with public health collaboration strongly agreed or agreed that they received electronically transmitted information needed to effectively treat COVID-19 from outside providers.

Fig. 1.

HIE by hospital and public health partnership. Authors' analyses using the linked data source of the 2020 AHA Annual Survey and IT Supplement. AHA, American Hospital Association; HIE, health information exchange; IT, Information Technology.

Figure 2 shows that hospitals with public health collaboration and affiliation with an ACO were the most likely to report availability of electronic clinical information and participate in local and national HIE networks. Hospitals with neither collaboration nor ACO affiliation were the least likely (16%) to report receiving electronically transmitted information to treat COVID-19, compared with 58% of hospitals with public health collaboration and ACO affiliation.

Fig. 2.

HIE by hospital and public health partnership and ACO affiliation. Authors' analyses using the linked data source of the 2020 AHA Annual Survey and IT Supplement. ACO, Accountable Care Organization.

Table 1 compares hospital summary statistics by public health collaboration and ACO affiliation. Hospitals with public health partnerships and ACO affiliation were more likely to be not-for-profit hospitals, with large bed size, and located in metro areas and areas with low SVI. Hospitals with neither public health collaboration nor ACO affiliation were the most likely to be in counties with the highest SVI (the poorest) and rural areas.

Table 1.

Summary Statistics by Hospital–Public Health Collaboration and Accountable Care Organization Affiliation

	NO HOSPITAL–PUBLIC HEALTH COLLABORATION AND NO ACO AFFILIATION		HOSPITAL–PUBLIC HEALTH COLLABORATION, BUT NO ACO AFFILIATION			NO HOSPITAL–PUBLIC HEALTH COLLABORATION, BUT WITH ACO AFFILIATION			HOSPITAL–PUBLIC HEALTH COLLABORATION AND ACO AFFILIATION
	n = 103		n = 323			n = 73			n = 969
	MEAN	STD	MEAN	STD	p	MEAN	STD	p	MEAN	STD	p
Not-for-profit hospital	0.43	0.50	0.62	0.49	<0.001	0.74	0.44	<0.001	0.86	0.35	<0.001
For-profit hospital	0.19	0.40	0.12	0.32	0.049	0.05	0.23	0.008	0.02	0.13	<0.001
Government-owned hospital	0.38	0.49	0.27	0.44	0.0292	0.21	0.41	0.014	0.13	0.33	<0.001
Bed size <100	0.50	0.50	0.39	0.49	0.068	0.38	0.49	0.144	0.27	0.45	<0.001
Bed size 100–400	0.28	0.45	0.34	0.47	0.267	0.37	0.49	0.218	0.32	0.47	0.403
Bed size ≥400	0.22	0.42	0.27	0.44	0.386	0.25	0.43	0.721	0.40	0.49	<0.001
Teaching hospital	0.02	0.14	0.07	0.25	0.075	0.01	0.12	0.774	0.11	0.31	0.005
Metro area	0.45	0.50	0.52	0.50	0.195	0.63	0.49	0.016	0.70	0.46	<0.001
Micropolitan area	0.21	0.41	0.21	0.41	0.947	0.18	0.39	0.031	0.16	0.37	<0.001
Rural area	0.34	0.48	0.27	0.44	0.169	0.19	0.40	0.564	0.14	0.35	0.155
SVI under the 25th percentile	0.19	0.40	0.22	0.42	0.538	0.33	0.47	0.042	0.26	0.44	0.156
SVI 25th–50th percentile	0.16	0.36	0.22	0.41	0.158	0.26	0.44	0.087	0.27	0.44	0.012
SVI 50th–75th percentile	0.26	0.44	0.28	0.45	0.745	0.26	0.44	0.978	0.25	0.43	0.783
SVI above the 75th percentile	0.39	0.49	0.28	0.45	0.035	0.15	0.36	<0.001	0.22	0.42	<0.001

Data source: the linked data source of the 2020 AHA Annual Survey and IT Supplement.

No hospital–public health collaboration and no ACO affiliation: the reference group.

ACO, Accountable Care Organization; AHA, American Hospital Association; IT, Information Technology; STD, standard deviation; SVI, Social Vulnerability Index.

Table 2 shows results of the cross-tabulation of hospital–public health collaboration and ACO affiliation with hospital HIE measures after controlling for all covariates and applying the state fixed-effects model. Compared with hospitals with neither public health collaboration nor ACO affiliation, hospitals with public health collaboration and ACO affiliation were 9% more likely to report electronically available clinical information from outside providers (marginal effect [ME] = 0.09, p = 0.04) and 9% more likely to participate in local HIE networks (ME = 0.09, p < 0.001) and national HIE networks (ME = 0.09, p = 0.02).

Table 2.

Health Information Exchange by Hospital–Public Health Collaboration and Accountable Care Organization Affiliation

	CLINICAL INFORMATION AVAILABLE ELECTRONICALLY FROM OUTSIDE PROVIDERS		PARTICIPATION IN LOCAL HIE NETWORKS		PARTICIPATION IN NATIONAL HIE NETWORKS		RECEIVED ELECTRONICALLY TRANSMITTED INFORMATION TO TREAT COVID-19		FREQUENTLY RECEIVED CLINICAL INFORMATION TO TREAT PATIENTS WITH COVID-19 ELECTRONICALLY
	ME	p	ME	p	ME	p	ME	p	ME	p
No hospital–public health collaboration and no ACO affiliation	Reference		Reference		Reference		Reference		Reference
Hospital–public health collaboration, but no ACO affiliation	−0.02	0.62	0.02	0.54	0.01	0.77	0.18	0.01	0.09	0.09
No hospital–public health collaboration, but with ACO affiliation	0.01	0.89	0.04	0.30	0.02	0.64	0.12	0.14	0.08	0.20
Hospital–public health collaboration and ACO affiliation	0.09	0.04	0.09	<0.001	0.09	0.02	0.30	<0.001	0.12	0.02
Not-for-profit hospital	Reference		Reference		Reference		Reference		Reference
For-profit hospital	−0.02	0.63	−0.19	<0.001	−0.30	<0.001	−0.47	<0.001	−0.29	<0.001
Government-owned hospital	−0.10	<0.001	−0.03	0.24	−0.04	0.15	−0.11	<0.001	−0.03	0.34
Bed size <100	Reference		Reference		Reference		Reference		Reference
Bed size 100–400	0.03	0.32	0.03	0.19	−0.01	0.61	0.02	0.62	0.05	0.07
Bed size ≥400	0.10	0.01	0.07	0.01	0.04	0.15	0.01	0.79	0.05	0.08
Teaching hospital	0.01	0.85	0.02	0.60	0.02	0.61	0.14	<0.001	0.01	0.70
Metro area	Reference		Reference		Reference		Reference		Reference
Rural area	−0.08	0.02	0.01	0.60	−0.03	0.26	−0.04	0.39	0.09	<0.001
Micropolitan area	−0.06	0.09	0.00	0.94	−0.06	0.03	−0.04	0.23	0.05	0.06
SVI under the 25th percentile	Reference		Reference		Reference		Reference		Reference
SVI 25th–50th percentile	0.00	0.91	0.03	0.29	0.01	0.63	0.01	0.88	−0.04	0.10
SVI 50th–75th percentile	−0.02	0.66	0.08	0.01	0.04	0.17	0.04	0.22	0.00	0.90
SVI above the 75th percentile	−0.05	0.21	0.06	0.03	0.02	0.54	−0.07	0.08	−0.06	0.06

Data source: the linked data source of the 2020 AHA Annual Survey and IT Supplement.

The specific survey questions for each outcome measure cited from the 2020 AHA IT Supplement:

• Clinical information available electronically from outside providers: “When treating a patient that was seen by a provider outside your organization or hospital system, do providers at your hospital routinely have necessary clinical information available electronically (not e-Fax) from outside providers or sources when treating a patient that was seen by another healthcare provider/setting?”

• Participation in local HIE networks: “Please indicate your level of participation in a state, regional, and/or local health information exchange (HIE) or health information organization (HIO).”

• Participation in national HIE networks: responses of “do not participate in any national health information exchange networks” to the question “Which of the following national health information exchange networks does your hospital currently actively participate in (i.e., operational exchange)?”

• Received electronically transmitted information to treat COVID-19: responses of “strongly agree or agree” to the statement “My hospital electronically received information from outside providers needed to effectively treat COVID-19.”

• Frequently received clinical information to treat patients with COVID-19 electronically: the survey question was “How frequently is each type of clinical information that is necessary for treating patients with COVID-19 electronically available (not e-Fax) from outside providers or other sources at the point of care?” Response of “always/often” to information type, including diagnoses, problem lists, laboratory results, clinical notes, medications, images, and immunization details.

The sample sizes for each regression varied: n = 1,375 for clinical information available electronically from outside providers; n = 1,436 for participation in local HIE networks; n = 1,384 for participation in national HIE networks; n = 1,316 for received electronically transmitted information to treat COVID-19; and n = 1,363 for frequently received clinical information to treat patients with COVID-19 electronically.

HIE, health information exchange; ME, marginal effect.

In addition, hospitals with public health collaboration and ACO affiliation were 30% (ME = 0.30, p < 0.001) and hospitals with public health collaboration, but no ACO affiliation, were 18% (ME = 0.12, p = 0.01) more likely to agree that they received electronically transmitted information needed to effectively treat COVID-19 from outside providers. Hospitals with public health collaboration and ACO affiliation were 12% (ME = 0.12, p = 0.02) more likely to report that they always/often received clinical information to treat patients with COVID-19 electronically.

Table 3 presents the decomposition results by two groups: hospitals located in areas with SVI above the 75th percentile (the most disadvantaged areas) versus below the 75th percentile. The decomposition model predicted HIE measures first. The predicted values of SVI above the 75th percentile and below the 75th percentile were statistically significant for clinical information available electronically from outside providers, participation in national HIE networks, and electronically transmitted information to treat COVID-19.

Table 3.

Decomposition Results of Predictors of Hospital-Based Health Information Exchange Measures

	CLINICAL INFORMATION AVAILABLE ELECTRONICALLY FROM OUTSIDE PROVIDERS		PARTICIPATION IN NATIONAL HIE NETWORKS		RECEIVED ELECTRONICALLY TRANSMITTED INFORMATION TO TREAT COVID-19
	PREDICTED PROBABILITY	p	PREDICTED PROBABILITY	p	PREDICTED PROBABILITY	p
SVI below the 75th percentile	0.75	<0.001	0.82	<0.001	0.52	<0.001
SVI above the 75th percentile	0.66	<0.001	0.76	<0.001	0.38	<0.001
Total difference	0.09	<0.001	0.05	0.04	0.14	<0.001

	% EXPLAINED DIFFERENCE	p	% EXPLAINED DIFFERENCE	p	% EXPLAINED DIFFERENCE	p
Total difference explained	24.17	0.07	89.19	<0.001	34.50	<0.001
Collaboration and ACO
Hospital–public health collaboration, but no ACO affiliation	−5.23	0.62	−4.43	0.52	−7.38	0.32
No hospital–public health collaboration, but with ACO affiliation	2.93	0.74	2.81	0.40	1.16	0.67
Hospital–public health collaboration and ACO affiliation	60.81	0.05	27.56	0.04	46.28	0.02

Authors' analyses using the linked data source of the 2020 AHA Annual Survey and IT Supplement.

Results further decomposed the association of each individual factor with these three outcomes. Among all the controlled variables, public health collaboration and ACO affiliation explained 28% and 46% of the differences in participation in national HIE networks and electronically transmitted information to treat COVID-19 by SVI groups.

Discussion

Our study found that hospitals that participated in public health partnerships and ACOs were more likely to have greater availability of electronic health data exchange. Hospitals that collaborated with the PHS and were affiliated with ACOs were more likely to receive electronically transmitted clinical information from outside providers, participate in local or national HIE networks, and report receiving electronic information to treat COVID-19. In addition, the patients and communities served by these health systems may benefit from not only the direct impact of PHS collaboration but also from the “halo” effects of the infrastructure that enables such partnership and collaboration.

During the COVID-19 era, PHSs played a critical role in delivering health care by fostering cross-sector collaboration and community resilience. For instance, CommunityConnect, a Whole Person Care program in Contra Costa County, California, is part of a larger California initiative aimed at connecting Medicaid beneficiaries with health risks to care coordination, social services, mental health care, and substance use treatment.²³ The program is based on partnership between public health, medical provider, and social service organizations.

Brewster et al. argue that the program enhances community resilience by promoting cross-sector relationships, which facilitated coordinated disaster responses, and by establishing a shared electronic health record and data infrastructure that could be used to identify vulnerable patients, among other benefits. The authors suggest that investing in integrated infrastructure is crucial for building resilient population health.²³

Researchers have concluded that investing in health IT infrastructure is critical for strengthening the PHS and enabling timely and impactful public health interventions during public health emergencies such as COVID-19.^24,25 Madhavan et al. describe the need for a transition to “digital public health,” which involves streamlining the coding and transmission of aggregate and personal clinical data and creating a PHS capable of supporting large-scale coordination.²⁴

Moreover, Holmgren et al. suggest that recent federal funding has been directed to hospital HIT without corresponding investment in the ability of public health agencies to receive and act on these data.²⁵ They found that the most significant barrier reported by hospitals to effective syndromic surveillance during the COVID-19 pandemic was public health agencies' inability to receive electronic data, although the team notes geographic variation in these challenges.

Technological capability is crucial to the success of value-based payment models and improving health equity, as described by Bleser et al. Thus, HIT infrastructure investments are needed to improve public health emergency planning and policymaking.²⁶ Bleser identified that data standardization directives from the ONC can strengthen cross-sector data exchange. To enable software for communication and facilitating HIE, implementing data standards such as application programming interfaces (APIs) and the Fast Healthcare Interoperability Resources is crucial.

In 2020, ONC and the Department of Health and Human Services (HHS) published the 21st Century Cures Act Final Rule, which aims to improve security and use of electronic health information through adoption of a standardized API across the health care industry. Furthermore, the final rule implements provisions to promote health information interoperability and prohibit information blocking to support the seamless and secure use and exchange of health -information.²⁷

Our study revealed that hospitals that had neither public health collaboration nor ACO affiliation were more likely to be located in areas with high SVI scores. These hospitals also reported less availability and capacity to share health data. This finding is consistent with the literature suggesting that hospitals in high vulnerability areas typically have the least capacity, but the greatest need to collaborate with public health and community partners to improve patient health.^28,29

Reaching patients in areas with SVI above the 75th percentile may require targeted integrated approaches that involve public health partnership, which are strengthened by effective IT infrastructure. It is critical to embed equity and inclusion in the development and strengthening of our health care system.³⁰

CMS has set a clear goal for the future of health care in the U.S., covering all traditional Medicare beneficiaries and most Medicaid beneficiaries in accountable care relationships by 2030.¹⁶ This move toward alternative payment models and value-based care has the potential to support small community and rural hospitals by relieving the pressure to compete for volume. Studies suggest that this evolution may provide greater stability for hospitals that lag behind in infrastructure and could also incentivize them to invest in IT.³¹

Our study has several limitations that should be considered. First, our analysis only included hospitals that responded to the AHA IT Supplement and thus our findings may not be generalizable to all hospitals in the U.S. Second, as our study used a cross-sectional design, it is important to note that we cannot establish causality between PHS collaboration and ACO affiliation with HIE.

Future research should examine causal relationships between these variables, focus on specific HIE functionalities, and seek to identify relationships pertaining to the exchange capabilities of specific types of EHR data. In addition, while we controlled for several covariates, a majority of differences in hospital electronic data exchange capabilities, such as the ability to receive clinical information from outside providers and to receive information to treat COVID-19, could not be explained.

Further research is needed to explore potential predictors of hospital HIE adoption, including the levels and types of collaborations and the availability of community resources for partnerships. Finally, future research should explore the long-term effects of HIE on health outcomes, considering the pandemic as an exogenous shock on health outcomes, to gain a more comprehensive understanding of its impact, which is beyond the scope of our present study.

POLICY IMPLICATIONS

Our study highlights the importance of engaging public health in collaborative, value-based care approaches, which can be cost-effective and promote population health and equity in the long run. Ongoing and emerging state and federal policies are being implemented to improve cross-sector collaboration, data interoperability, and care coordination.

As health care systems evolve, it is imperative that we prioritize investments in IT infrastructure, particularly in hospitals with high vulnerability scores and in geographic regions with limited public health collaboration and ACO affiliation. By doing so, we can better support hospitals' capacity to communicate and work with public health and community partners, ultimately improving patient health outcomes. Further research is needed to identify causality and explore the availability of community resources to partner with as well as additional predictors of hospital HIE adoption.

In November 2022, the CDC awarded $3.2 billion directly to state, local, and territorial health departments to help strengthen the public health workforce and infrastructure, which represents a promising step toward better integration.³² Standardization and implementation guidance from federal agencies will be important to help states and local PHSs align on synergistic strategies.

Another promising development is the regulation that went into effect in October 2022 that requires hospitals to exchange U.S. Core Data for Interoperability, which the former National Coordinator for Health Information Technology, Dr. David Blumenthal, called an important step toward achieving effective exchange of health information that can improve the quality of health care and reduce costs.³ Improved HIT interoperability can also aid in public health emergency preparedness and policymaking and facilitate smoother care coordination and referrals, thereby improving access to care.

Regarding access to care, a recent policy was passed as a result of the COVID-19 pandemic: the December 2022 spending bill extended Medicare telehealth provisions until the end of 2024, further expanding access to care.³³

The combination of regulatory requirements (such as the rule to share U.S. Core Data for Interoperability) and new programming incentives provide opportunities to promote cross-sector collaboration, equity, and more effective PHSs responses during states of emergency. For example, ACO Realizing Equity, Access, and Community Health (REACH) is the first federal value-based payment model focused on equity, which may lead to greater participation from ACOs and more incentives in the future.¹⁵

Conclusions

The COVID-19 pandemic exposed significant gaps in health data interoperability, reporting, and infrastructure. It emphasized the urgent need for health equity and tailored programs that can effectively reach minoritized and vulnerable populations. The pandemic also revealed cracks in the health care workforce and the PHS. It is essential to investigate integrative and collaborative approaches that encourage cooperation between providers and the PHS, particularly through alternative payment models such as ACOs.

Future research should examine the outcomes of collaborative care models and the role of HIT and clinical data availability in achieving program success.

Footnotes

Authors' Contributions

Conception and design; acquisition, analysis, and interpretation of data; drafting of the article; and critical revision of the article for important intellectual content were done by J.C., S.Y, and T.K.M. J.C. obtained funding; provided administrative, technical, or material support; and performed the supervision.

Disclosure Statement

No competing financial interests exist.

Funding Information

This study is supported by the National Institute on Aging (R01AG62315) and the National Institute on Minority Health and Health Disparities (R01MD011523).

References

Greene

, McClintock

, Durant

TJS

. Interoperability: COVID-19 as an impetus for change. Clin Chem, 2021; 67(4):592–595; doi: 10.1093/clinchem/hvab006

Sittig

, Singh

. COVID-19 and the need for a national health information technology infrastructure. JAMA, 2020; 323(23):2373–2374; doi: 10.1001/jama.2020.7239

Blumenthal

. A step toward interoperability of health IT. N Engl J Med, 2022; 387(24):2201–2203; doi: 10.1056/NEJMp2213873

Williams

, Mostashari

, Mertz

, et al. From the office of the national coordinator: The strategy for advancing the exchange of health information. Health Aff, 2012; 31(3):527–536; doi: 10.1377/hlthaff.2011.1314

Adler-Milstein

, Garg

, Zhao

, et al. A survey of health information exchange organizations in advance of a nationwide connectivity framework. Health Aff, 2021; 40(5):736–744; doi: 10.1377/hlthaff.2020.01497

Patel

, Ranasinghe

, Phan

, et al. Public health is local. Health Aff, 2022; doi: 10.1377/forefront.20221129.996214

Novak

, Bloodworth

, Green

, et al. Local health department activities to reduce emergency department visits for substance use disorders. J Healthc Qual, 2019; 41(3):134–145; doi: 10.1097/JHQ.0000000000000161

Chen

, Novak

, Barath

, et al. Local health departments' promotion of mental health care and reductions in 30-day all-cause readmission rates in Maryland. Med Care, 2018; 56(2):153–161; doi: 10.1097/MLR.0000000000000850

Chen

, Novak

, Goldman

. Public health system-delivered mental health preventive care links to significant reduction of health care costs. Popul Health Manag, 2018; 21(6):462–468; doi: 10.1089/pop.2018.0010

10.

Chen

, Bloodworth

, Novak

, et al. Reducing preventable hospitalization and disparity: Association with local health department mental health promotion activities. Am J Prev Med, 2018; 54(1):103–112; doi: 10.1016/j.amepre.2017.10.011

11.

Ingram

, Scutchfield

, Charnigo

, et al. Local public health system performance and community health outcomes. Am J Prev Med, 2012; 42(3):214–220; doi: 10.1016/j.amepre.2011.10.022

12.

Grembowski

, Bekemeier

, Conrad

, et al. Are local health department expenditures related to racial disparities in mortality?. Soc Sci Med, 2010; 71(12):2057–2065; doi: 10.1016/j.socscimed.2010.09.004

13.

Emanuel

, Osterholm

, Gounder

. A national strategy for the “new normal” of life with COVID. JAMA, 2022; 327(3):211; doi: 10.1001/jama.2021.24282

14.

Hearld

, Carroll

, Hall

. The adoption and spread of hospital care coordination activities under value-based programs. Am J Manag Care, 2019; 25(8):397–404.

15.

Muhlestein

, Saunders

, de Lisle

, et al. Growth of value-based care and accountable care organizations in 2022. Health Aff, 2022; doi: 10.1377/forefront.20221130.22253

16.

Centers for Medicare and Medicaid Innovation Center. Innovation Center Strategy Refresh. Woodlawn, MD; 2021. Available from: https://innovation.cms.gov/strategic-direction-whitepaper [Last accessed: January 1, 2023 ].

17.

American Hospital Association. AHA Annual Survey Database. Washington, DC; n.d. Available from: https://www.ahadata.com/aha-annual-survey-database [Last accessed: December 13, 2022 ].

18.

American Hospital Association. AHA Healthcare IT Database. Washington, DC; n.d. Available from: https://www.ahadata.com/aha-healthcare-it-database [Last accessed: April 14, 2023 ].

19.

Nayak

, Islam

, Mehta

, et al. Impact of social vulnerability on COVID-19 incidence and outcomes in the United States. medRxiv, 2020; 2020.04.10.20060962; doi: 10.1101/2020.04.10.20060962

20.

Lehnert

, Wilt

, Flanagan

, et al. Spatial exploration of the CDC's Social Vulnerability Index and heat-related health outcomes in Georgia. Int J Disaster Risk Reduct, 2020; 46:10.1016/j.ijdrr.2020.101517; doi: 10.1016/j.ijdrr.2020.101517

21.

Chen

, Spencer

MRT

, Buchongo

. Strengthening the public health partnership and telehealth infrastructure to reduce health care disparities. Popul Health Manag, 2022; 25(6):814–821; doi: 10.1089/pop.2022.0166

22.

Centers for Disease Control. CDC/ATSDR Social Vulnerability Index. Atlanta, GA; 2023. Available from: https://www.atsdr.cdc.gov/placeandhealth/svi/index.html [Last accessed: January 1, 2023 ].

23.

Brewster

, Fleming

, Knox

, et al. Community resilience for COVID-19 and beyond: Leveraging a system for health care and social services integration. Health Aff, 2020; doi: 10.1377/forefront.20200807.222833

24.

Madhavan

, Bastarache

, Brown

, et al. Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: A perspective from 15 academic medical centers. J Am Med Inform Assoc, 2021; 28(2):393–401; doi: 10.1093/jamia/ocaa287

25.

Holmgren

, Apathy

, Adler-Milstein

. Barriers to hospital electronic public health reporting and implications for the COVID-19 pandemic. J Am Med Inform Assoc, 2020; 27(8):1306–1309; doi: 10.1093/jamia/ocaa112

26.

Bleser

. Advancing equity through value-based payment: Implementation and evaluation to support design goals. Health Aff, 2022; doi: 10.1377/forefront.20221103.813959

27.

HealthIT.gov. ONC's Cures Act Final Rule. Washington, DC. Available from: https://www.healthit.gov/topic/oncs-cures-act-final-rule [Last accessed: January 1, 2023 ].

28.

Islam

, Malla

, Yeh

, et al. County-level social vulnerability is associated with in-hospital death and major adverse cardiovascular events in patients hospitalized with COVID-19: An analysis of the American Heart Association COVID-19 cardiovascular disease registry. Circ Cardiovasc Qual Outcomes, 2022; 15(8):e008612doi: 10.1161/CIRCOUTCOMES.121.008612

29.

Melvin

, Wiggins

, Burse

, et al. The role of public health in COVID-19 emergency response efforts from a rural health perspective. Prev Chronic Dis, 2020; 17:200256; doi: 10.5888/pcd17.200256

30.

MacDonald

, Demiris

, Shevin

, et al. Health technology for all: An equity-based paradigm shift opportunity. NAM Perspect, 2022; 12; doi: 10.31478/202212a

31.

Bipartisan Policy Center. Reinventing Rural Health Care: A Case Study of Seven Upper Midwest States. Washington, DC; 2018. Available from: https://bipartisanpolicy.org/event/reinventing-rural-health-care-a-case-study-of-7-upper-midwest-states/ [Last accessed: January 1, 2023 ].

32.

Centers for Disease Control and Prevention. CDC Awards More Than $3 Billion to Improve U.S. Public Health Workforce and Infrastructure. Atlanta, GA; 2022. Available from: https://www.cdc.gov/media/releases/2022/p1129-cdc-infrastructure.html [Last accessed: January 1, 2023 ].

33.

Zebley

. American Telemedicine Association. Telehealth Policy Update. Washington, DC; 2023. Available from: https://www.americantelemed.org/resources/telehealth-policy-update-january-2023/ [Last accessed: January 28, 2023 ].