Communities led by health information organizations were more likely than those led by healthcare organizations to receive ongoing funding for implementing health information technology.
To determine potential predictors of sustainability among community-based organizations that are implementing health information technology (HIT) with health information exchange, in a state with significant funding of such organizations.
A longitudinal cohort study of community-based organizations funded through the first phase of the $440 million Healthcare Efficiency and Affordability Law for New Yorkers program.
We administered a baseline telephone survey in January and February 2007, using a novel instrument with open-ended questions, and collectedfollow-up data from the New York State Department of Health regarding subsequent funding awarded in March 2008. We used logistic regression to determine associations between 18 organizational characteristics and subsequent funding.
All 26 organizations (100%) responded. Having the alliance led by a health information organization (odds ratio [OR] 11.4, P = .01) and having performed a community-based needs assessment (OR 5.1, P = .08) increased the unadjusted odds of subsequent funding. Having the intervention target the long-term care setting (OR 0.14, P = .03) decreased the unadjusted odds of subsequent funding. In the multivariate model, having the alliance led by a health information organization, rather than a healthcare organization, increased the odds of subsequent funding (adjusted OR 6.4; 95% confidence interval 0.8, 52.6; P = .08).
Results from this longitudinal study suggest that both health information organizations and healthcare organizations are needed for sustainable HIT transformation.
(Am J Manag Care. 2011;17(4):290-295)
Implementing health information technology (HIT) is a national priority. However, the sustainability of community-based HIT implementation efforts is unclear.
Through the American Recovery and Reinvestment Act of 2009, the federal government is providing approximately $17 billion in financial incentives to physicians and hospitals for meaningful use of electronic health records (EHRs) that support the exchange of data electronically.1 The combination of EHRs and health information exchange is expected to decrease fragmentation of healthcare and improve coordination of care.2 However, the optimal strategy for how best to implement EHRs and health information exchange is not known. Moreover, early experiences with community-based strategies for achieving EHR implementation and health information exchange have been fraught with failures.3,4
New York State has been embarking on an ambitious and novel program since 2005 called the Healthcare Efficiency and Affordability Law for New Yorkers (HEAL NY), through which it is investing approximately $440 million in interoperable health information technology (HIT), with a focus on EHRs and health information exchange.5 This represents the largest state-based investment in the country.6-8 In addition, New York’s initiative includes $280 million in matching funds from grantee communities and $120 million in matching funds from other state and federal programs.9 Beyond financial support, New York State is also providing strategic direction, collaborative assistance, and technical support.
New York State’s program relies in large part on community-based alliances that come together for the purpose of EHR implementation and health information exchange. We found previously that 100% of alliances funded under the first phase of HEAL NY (N = 26) were in existence 2 years after the start of the program, which is a much higher rate of survival than that found nationally.10 However, only 11 of the 26 alliances received funding in the next phase of HEAL NY.10 Our previous study did not determine which alliances were most likely to receive funding in the second round. Understanding predictors of continued funding—as potential markers of early sustainability—could inform similar state-based and community-based initiatives nationwide.
Our objective was to determine which baseline characteristics of HEAL NY grantees were associated with early sustainability, as demonstrated by receiving subsequent HEAL NY funding.
We conducted a longitudinal study of the 26 community-based alliances funded through the first phase of HEAL NY. Baseline data were collected through a telephone survey conducted over a 5-week period in January and February 2007. (At that time, grantees had been notified of their awards but had not yet received funding from New York State.) We supplemented baseline survey data with publicly available data about the grantees from the New York State Department of Health Web site.11 We obtained follow-up data from the Department of Health Web site, which announced the status of funding for HEAL NY Phase 5 in March 2008.12 (Note that HEAL NY Phase 5 was the second phase of HIT funding, as HEAL NY Phases 2 and 4 were not related to HIT, and Phase 3 was withdrawn, with funds reallocated to HEAL 5.) The Institutional Review Board of Weill Cornell Medical College approved this study.
The HEAL NY program selected grantees based on responses to public Requests for Grant Applications.5,13 Grantees were community-based alliances that included stakeholders of various types not under the same corporate structure (eg, hospitals, physician practices, health plans). The lead entity for such an alliance could either be a health information organization (eg, a regional health information organization) or a healthcare organization (eg, a hospital, an independent practice association). Grantees were required to provide funds to match any state grant in at least a 50:50 ratio for HEAL 1 and a 25:75 (grantee to state) ratio for HEAL 5.
Projects for HEAL 1 were required to address adoption of EHRs, support of electronic prescribing, and/or development and implementation of community-wide clinical data exchanges. Projects for HEAL 5 were required to address adoption of EHRs, development and implementation of community-wide clinical data exchanges, and development and implementation of clinical information services. Criteria for receiving funding under HEAL 5, which were announced in October 2007 (after baseline data for this study were collected), were more stringent than those under HEAL 1, requiring more advanced governance structures, business models, and willingness to participate in a statewide collaboration process.10
This survey was conducted as part of a broader effort by the Health Information Technology Evaluation Collaborative (HITEC). HITEC is a multi-institutional academic collaborative established to conduct independent and rigorous evaluation of New York State’s HIT initiatives.14 The authors include directors and members of HITEC.
We included all 26 HEAL 1 grantees. Using a standardized telephone script, we invited the leader of each grantee to participate. Prospective participants were told that HITEC was conducting a study “to tell the story of HEAL NY” and that responses were voluntary.
No previously published instrument was suitable for this study. We therefore created a novel instrument with 26 openended questions about the activities of the grantees (). The instrument was pilot-tested internally and refined for clarity. The questions were divided into 9 sections: overview of the planned interventions (including project goals, clinical settings, clinical issues, and use case analysis), EHRs and electronic prescribing, health information exchange, organization and governance, privacy policies, timetables for implementation, financial sustainability, expected impact, and evaluation.
Each telephone interview was conducted by 1 of 4 HITEC members (LMK, ABW, JS, KY-F). Each interview was confirmed in advance by an e-mail that included (1) the surveyinstrument, (2) disclosures that some of the members of HITEC were involved in organizations funded through HEAL 1 and some were involved in applications for HEAL 5, and (3) notification that representatives from the New York State Department of Health would have access to the data collected. Each telephone interview lasted 30 to 75 minutes and was audio-recorded.
Two investigators (LMK, RK) created domains a priori for categorization of survey data. Each investigator who conducted interviews compiled typed notes of his/her interviews. One investigator (LMK) assembled these notes into a summary of results overall, including categorization of responses to questions into the previously constructed domains. Categorization into multiple, non—mutually exclusive domains was permitted. The other interviewers and members of the study team reviewed this summary for clarity and face validity.
In addition, we classified the grantees into 2 organization types, based on the type of organization that served as the grantee’s lead applicant: healthcare organization or health information organization.
We considered 18 potential predictors of HEAL 5 funding, which are shown in the . We used bivariate and multivariate logistic regression to determine associations between the potential predictors and the receipt of HEAL 5 funding (yes/no). Potential predictors with bivariate P values of <.20 were entered into the multivariate model. We used backward stepwise elimination to generate the most parsimonious model. We excluded from the model the variable of “pursuit of health information exchange,” because this was true for all grantees and thus could not be used to distinguish grantees from one another. We also excluded the size of the HEAL 1 award as a potential predictor, because this variable was not conceptually informative; that is, a larger HEAL 1 award may have indicated a more successful organization, but it did not explain why the organization was successful.
Analysis was conducted using Excel (Microsoft Corp, Redmond, WA), Stata version 9 (StataCorp, College Station, TX), and SAS version 9.2 (SAS Institute, Cary, NC).
Characteristics of the Grantees
All 26 HEAL 1 grantees participated (100% response rate). The grantees received awards ranging in size from $177,503 to $5,000,000 each, with a mean award of $1.8 million (median $1.7 million). Grantees were distributed across 6 geographical regions of New York State (Table).
Respondents listed a variety of stakeholders as participants in their organizations, with the most common being physician practices (mean 5.6 practices per grantee), community hospitals (mean 2.4), community health centers (mean 1.5), health plans (mean 1.4), and long-term care facilities (mean 1.1). Two-thirds of grantees (69%) did not include an academic medical center, as defined as a tertiary care teaching hospital with an on-site medical school.
Half (50%) of the 26 grantees described themselves as having a formal governance structure. Fifteen percent reported having no formal governance structure and no plans to create one. The remainder (35%) reported either that formal governance structures were in the process of being created or that the governance structure for the grantee organization was composed of 1 or more committees housed inside a much larger healthcare organization.
Approximately two-thirds of grantees (65%) were led by healthcare organizations. The other one-third (35%) were led by health information organizations formed expressly for the purpose of implementing health information exchange among the stakeholder organizations.
The majority of grantees (80%) reported working on developing appropriate privacy policies specific to their HEAL NY initiatives. Two (8%) reported already having created privacy policies specific to their HEAL NY initiatives. Three (12%) reported that they would rely on more general existing privacy policies rather than pursue creation of specific new policies.
Nearly two-thirds of grantees (62%) reported having performed a community-based needs assessment, which was defined as “any analysis your organization has done to frame the clinical problem and the potential benefit offered by your project.” Examples given of such analyses included surveys of providers to assess clinical needs and work flow, surveys of patients to assess comfort with various technologies, and projected return-on-investment analyses by external consultants. The clinical problems that the grantees reported trying to address included the inability to access remote data, gaps in communication between providers, suboptimal healthcare quality, and inefficient care.
Characteristics of the Planned Interventions
All 26 grantees (100%) reported their intent to implement health information exchange. In addition, 17 (65%) planned implementation of EHRs, 17 (65%) planned implementation of electronic prescribing, and 12 (46%) planned implementation of both EHRs and electronic prescribing.
The most common clinical settings targeted were outpatient (85%), inpatient (65%), and long-term care (54%). Less common clinical settings included home care (34%) and the emergency department (27%). Nearly half the grantees (46%) were specifically targeting transitions across care settings.
Approximately half (57%) of the grantees that planned to implement EHRs or electronic prescribing and two-thirds (69%) of those that planned to implement health information exchange were able to articulate a timetable for implementation (that is, specify the month and year during which their technology was expected to “go live”).
When asked directly whether their interventions were expected to save money, 65% said yes, 8% said no, and 27% said they were not sure. The majority of grantees (69%) expected that financial savings would be achieved through improvements in healthcare efficiency. When asked to whom those financial savings would accrue, the most common answer was payers (50%). When asked directly whether they had a business model for financial sustainability, 38% said yes, 19% said no, and 42% said they were working on developing one.
Predictors of HEAL 5 Funding
When we considered the 18 potential predictors listed in the Table (not including pursuit of health information exchange), 3 had bivariate P values of <.20. Having the alliance led by a health information organization (odds ratio [OR] 11.4; 95% confidence interval [CI] 1.7, 78.4; P = .01) and having performed a community-based needs assessment (OR 5.1; 95% CI 0.8, 32.3; P = .08) were each associated with higher unadjusted odds of subsequent funding. Having an intervention targeting the long-term care setting was associated with lower unadjusted odds of subsequent funding (OR 0.14; 95% CI 0.02, 0.79; P = .03).
The final multivariate model yielded 1 variable with a trend: alliances led by health information organizations were 6 times more likely to receive subsequent funding than alli ances led by healthcare organizations (adjusted OR 6.4; 95% CI 0.8, 52.6; P = .08).
We conducted a longitudinal study of all 26 community-based alliances funded through the first phase of HEAL NY, a novel and ambitious program for the implementation of HIT in New York State. Of the 26 grantees, only 11 (42%) received funding under the next phase of New York State funding, HEAL 5. We found in this study that grantees led by health information organizations were 6 times more likely to receive subsequent funding than grantees led by healthcare organizations. The magnitude of this association is striking, especially given the small sample size of the study.
HEAL NY grantees generally had 1 of 2 organizational structures. Two-thirds of grantees (65%) were led by a healthcare organization (eg, a hospital, an independent practice association), which aligned with other clinical stakeholders for the purpose of health information exchange. One-third of grantees (35%) were led by a health information organization, whose primary mission was health information exchange and not direct clinical care. This second model appeared to be more successful in our study, in terms of obtaining subsequent New York State funding. We use the term “health information organization,” rather than “regional health information organization,” as not all of the grantees met a formal definition of regional health information organization due to a lack of formal governance structure at baseline.15 Regional health information organizations convene multiple clinical stakeholders in a given geographic area to pursue health information exchange with a formal governance model.15 Almost all of the HEAL 1 grantees that were “health information organizations” developed into regional health information organizations and were recognized as such under HEAL 5.10
It is not clear from the data collected in this study why health information organizations were more successful than healthcare organizations. There are several possible explanations. First, health information organizations may be able to convene competing healthcare stakeholders in a way that the healthcare stakeholders themselves cannot. Second, HIT may have been propelled further in organizations whose primary mission was its diffusion, especially as healthcare organizations appropriately have multiple competing clinical priorities. Third, there were historically insufficient financial incentives to cause focus on HIT and health information exchange, except in organizations whose developing business cases relied on its success.
The variables that were significant only in bivariate models are worth brief discussion, as these variables may merit further exploration in larger studies. Our finding that community- based needs assessments were associated with subsequent funding implies that communities that recognized, measured, and addressed clinical problems faced by their own providers were more successful than those that did not, perhaps because the needs assessment contributed to the strategic vision of the organization and its commitment to that vision. This suggests that physicians and hospitals will be most successful in earning meaningful-use incentives if they first measure the needs of their community and then choose those meaningful-use measures that are closely aligned with their needs. Our finding that targeting long-term care facilities decreased the odds of subsequent funding may indicate the high degree of difficulty in integrating these facilities into electronic networks, as their baseline HIT adoption rate is low.16
Our study has national implications as the country embarks on an unprecedented investment in HIT and HIT infrastructure. In addition to funding incentives for meaningful use of HIT, the Office of the National Coordinator for Health Information Technology is funding a nearly $550 million program in state-level health information exchange, a $250 million program in “Beacon” communities, and a $677 million program for Regional Extension Centers.17 All of these programs are designed to support the clinicians and hospitals that are adopting HIT and pursuing meaningful use. It is not clear, though, that these programs focus on convening clinical stakeholders for health information exchange. They emphasize governance, policies and standards, technical infrastructure, quality improvement, and technical support, all of which are important, but which together may still not be sufficient for enabling health information exchange.
Health information exchange requires the cooperation of multiple parties, including hospitals, clinicians, patients, commercial vendors, government, laboratories, radiology facilities, payers, public health departments, pharmacy benefit managers, and others. The scope of the task of health information exchange is huge and perhaps more complicated than implementation of EHRs. The challenges with health information exchange are as much political as they are medical or technical. This study provides empirical evidence to support the importance of health information organizations and suggests that their key function, to convene clinical stakeholders, is needed to advance HIT and health information exchange.
To our knowledge, this is one of the most detailed longitudinal studies of community-based organizations attempting to implement interoperable HIT. Another study considered predictors of financial viability for regional health information organizations,18 but that study did not consider organization type as a potential predictor, so it is difficult to compare that study with ours. A previous study we conducted suggested that formal governance structures were important to success ful community-based organizations pursuing HIT.10 The current study adds to our previous work by drawing attention to the importance of health information organizations.
This study has several limitations. First, the sample size was limited and the findings were of borderline statistical significance. However, the effect sizes we found were large, which suggests that results would have been statistically significant with a slightly larger sample size. Second, this study did not capture sources of funding other than HEAL NY grants, and it is possible that some grantees that were not funded under HEAL 5 received funding from other sources. Third, this study represents follow-up over a year; longer term follow-up is needed to demonstrate sustainability, especially sustainability independent of state grant funds. Fourth, this study did not capture several organizational characteristics that may be associated with sustainability, such as health plan involvement, the strength of individual leaders, and the history of collaboration in the communities. Finally, this study describes the New York experience and may not be representative of all states.
In conclusion, this study represents one of the most detailed longitudinal studies of community-based organizations pursuing HIT. We found that grantees led by health information organizations were more successful in obtaining future funding than those led by organizations whose primary mission was direct patient care. These findings are highly relevant nationally as we embark on an unprecedented investment to implement HIT and pursue meaningful use.
We thank the HEAL 1 grantees for their participation in the study. We also thank C. William Schroth, MBA, former consultant to the New York State Department of Health, and Rachel Block, Deputy Commissioner for Health IT Transformation for New York State and former Executive Director of the New York eHealth Collaborative, for their thoughtful comments. We acknowledge Ellen Flink, MBA, and Anna Colello, JD, from the New York State Department of Health for their assistance.
Author Affiliations: From Department of Public Health (LMK, KY-F, YB, RK), Weill Cornell Medical College, New York, NY; Department of Medicine (LMK, RK), Weill Cornell Medical College, New York, NY; Department of Biomedical Informatics (ABW, JS), Columbia University College of Physicians and Surgeons, New York, NY; Department of Emergency Medicine (JS), Mount Sinai School of Medicine, New York, NY; Department of Pediatrics (EA, RK), Weill Cornell Medical College, New York, NY; and New York-Presbyterian Hospital (EA, RK), New York, NY.
Funding Source: This work was supported by the Commonwealth Fund (grant #20060550).
Author Disclosures: Drs Kern and Kaushal report receiving grants to evaluate Healthcare Efficiency and Affordability Law for New Yorkers Phases 5, 10, and 17, and attending meetings convened by New York State. The other authors (ABW, JS, KY-F, EA,YB) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. An earlier version of this work was presented at the Society of General Internal Medicine 2008 Annual Meeting, Pittsburgh, PA, April 10, 2008.
Authorship Information: Concept and design (LMK, ABW, JS, EA, RK); acquisition of data (LMK, ABW, JS, KY-F, RK); analysis and interpretation of data (LMK, JS, EA, YB, RK); drafting of the manuscript (LMK); critical revision of the manuscript for important intellectual content (LMK, ABW, KY-F, EA, YB, RK); statistical analysis (LMK, YB); provision of study materials or patients (LMK, RK); obtaining funding (LMK, RK); administrative, technical, or logistic support (JS, KY-F); and supervision (LMK, RK).
Address correspondence to: Lisa M. Kern, MD, MPH, Department of Public Health, Weill Cornell Medical College, 402 East 67th St, New York, NY 10065. E-mail: firstname.lastname@example.org.
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