Abstract
This study identifies the type, distribution, and interactions of US hospitals that identify as electronic-data-driven, patient-centric, and learning-focused. Such facilities, termed Health Information Interested (HII) hospitals in this study, meet the defining criteria for one or more of the following designations: learning health systems (LHS), Health Information Technology for Economic and Clinical Health (HITECH) meaningful use stage three compliant (MU3), Patient-Centered Outcomes Research Institute (PCORI) funded, or medical home/safety net (MH/SN) hospital. The American Hospital Association (AHA) IT supplemental survey and other supporting data spanning 2013 to 2018 were used to identify HII hospitals. HII hospitals increased from 19.9 percent to 62.4 percent of AHA reporting hospitals from 2013 to 2018. HII subcategories in 2018 such as the full LHS (37.2 percent) and MU3 (46.9 percent) were dominant, with 33.2 percent having both designations. This indicates increased interest in patient-centric, learning-focused care using electronic health data. This information can enable health information management (HIM) professionals to be aware of programs or approaches that can facilitate learning-focused, patient-centric care using electronic health data within health systems.
Keywords: health information interested hospitals, learning health systems, electronic data driven, patient-centric care, learning focused, mHealth, health IT, medical home/safety nets, PCORI, meaningful use stage three
Introduction
As US healthcare organizations are increasingly incentivized to improve care processes, they are also becoming more invested in the capture and use of electronic health data, including patient generated health data.1,2 Patients are also calling for the increased use of electronic health data to inform their care processes and decisions.3 Payors and patients alike pressure the healthcare system to improve quality, manage multifaceted individual and population health needs, curb costs, and reduce inefficiencies, some of which have been attributed to care fragmentation from a lack of systemic and patient-centric approach to care.4-6
Expanding electronic health data collection and use is one strategy to address these challenges. However, this requires that new initiatives and programs be created and older ones adapted. One means to healthcare organization improvement is adopting a learning health system (LHS) approach. The LHS supports technology and electronic data-driven strategies that have the potential to mitigate care fragmentation and facilitate patient-centered care. The LHS facilitates commitment to patient outcome measures, which support continuous improvement in care processes.7
Other approaches to health system improvement through expanded use of technology and electronic data include federal incentive programs, such as the Health Information Technology Economic and Clinical Health (HITECH) Act. HITECH’s meaningful use stage three (MU3)8 incentivizes hospitals to utilize electronic data capture and patient-centric measures, including patient generated health data.9,10 Other programs that support or incentivize electronic data capture and patient-focused approaches include the Patient-Centered Outcomes Research Institute (PCORI) initiative, and the medical home/safety net (MH/SN) designation.11-14 Awareness of these approaches among health information management (HIM) professionals can facilitate an understanding of potential programs that can be adopted to facilitate electronic data-driven, patient-centric care in their organizations.
The Institute of Medicine (IOM) and the National Academy of Engineering have advocated for the use of electronic health data in continuous improvement efforts.15 Other research suggests that hospitals that have engaged with the aforementioned programs are more likely to demonstrate patient-centric and learning-focused approaches to care, and to use electronic health data to achieve their care and continuous improvement targets.16-22 The common health information technology (IT) related features that characterize LHS, MU3, PCORI, and MH/SN programs suggests that participating facilities can also be grouped together. Based on their participation in one or more of these initiatives, this study defines hospitals that are committed to electronic data-driven, patient-centric, and learning-focused continuous improvement processes using electronic health data as Health Information Interested (HII). However, to date there has not been a comprehensive inventory and assessment of hospitals participating in these initiatives and their interactions. This study assesses hospital participation in these initiatives and programs, considers how widespread they are, the rate at which hospitals are choosing to participate in one or more, and summarizes their patterns of participation.
Background
Senge (1990), in his book The Fifth Discipline, detailed his account of the “learning organization.” He defined learning organizations as “where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning how to learn together.”23 According to Senge, learning organizations can quickly adapt to changes and can secure more competitive advantages due to their focus on learning and systems thinking.24,25 The HII definition in this study was based on Senge’s learning organization theory. The criteria for HII hospitals included those with demonstrable interest in learning from electronically collected data to inform patient-centric care and continuous improvement in care processes. A review of the literature showed that the aforementioned four HII hospital types in this study are similar in their use of electronic health data to enable learning, continuous improvement in care and patient-centric approaches to care.26-30
HII Hospitals
The LHS was designed to address health systems’ needs for continuous improvement in care processes and is derived from the systemic approach described in Senge’s learning organization model.31 The LHS addresses some of the needs of healthcare organizations that seek to become learning organizations through the development of technology and data-driven learning infrastructure. As defined by the IOM, the LHS is an integrated health system “ ... in which progress in science, informatics, and care-culture align to generate new knowledge as an ongoing, natural by-product of the care experience, and seamlessly refine and deliver best practices for continuous improvement in health and healthcare.”32,33
LHS’s three core foundational components include an infrastructure for health related data capture, care improvement targets, and a supportive policy environment (Mullins et al., 2018). Ten core LHS values have also been identified as important for incorporating LHS tenets into population health improvements, and these include adaptability, scientific integrity, person-centeredness, inclusiveness, value, accessibility, governance, privacy, transparency, and cooperative and participatory leadership.34,35 Based on the LHS definition, core foundational components, and values, four key LHS principles were derived in this study: 1) capacity to collect and use electronic health data; 2) commitment to evidence/data-driven decision support; 3) patient-centered/data-driven quality improvement measures; and 4) the use of safe and certified electronic health data platforms (Table 1.1).36-38
In addition to the LHS, this study posits that PCORI-funded, MH/SN, and MU3-compliant hospitals can be described as HII hospitals and as learning organizations. As earlier stated, HII hospitals are patient-centric, electronic health data-driven, and learning-focused. These facilities have designed health IT, electronic health data-driven and technology infrastructure that enable them to adapt to changes and secure more competitive advantages by learning from patient-focused data for their care improvement targets, consistent with Senge’s learning organization theory. These hospital types can be described as HII hospitals because their daily operations include continuous improvement in care processes, patient-focused care, a systemic approach to care, and reflect the principles of Senge’s learning organization theory.
Methods
This study evaluated three years of hospital health IT data to describe the distribution of HII hospitals generally, across time periods, and among the four HII subtypes. Data from the American Hospital Association’s (AHA) 2013, 2016, and 2018 IT supplemental survey was used to identify LHS and MU3 hospitals.39-41 These data sets are made up of the responses of the 3,283; 3,656; and 3,540 US hospitals that participated in 2013, 2016, and 2018 AHA IT supplemental surveys, respectively. Other supporting data sets included the CMS innovation award funding list and the PCORI funded projects web list, which were used to identify MH/SN and PCORI-funded hospitals, respectively.42-45 MH/SN hospitals were initiated in 2013 to use innovative health IT tools to improve patient-centered care and care coordination.46 PCORI was established to fund US healthcare organizations to conduct research related to patient-related outcomes based on information systems and technology in order to contribute complementary data to clinician-derived metrics traditionally used to inform healthcare decision-making.47
LHS principles developed in this study were used to create the LHS criteria, and a measure of the degree of LHS practice per year of data was developed by identifying questions in the AHA IT supplemental survey that aligned with Friedman et al.’s conceptualization of LHS development.48 Table 1.2 provides the questions used from each year of the AHA IT supplemental survey to identify stages of LHS development. Only hospitals that met LHS stage three development, termed full LHS hospitals, were categorized as HII. Hospitals within the data that met the MU3 criteria; those that secured funding through PCORI to facilitate patient-centered, electronic data-driven, and learning-focused care or those that obtained the CMS innovation funds to become MH/SN between 2013 and 2018 were categorized as HII and included in the study. The prevalence of LHS hospitals across the three stages, their change over the study period, the prevalence of the other HII hospitals, and the key subcategory interactions observed through cross-tabulations are presented as part of study results.
Results
Learning Health Systems Practicing Hospitals
LHS hospitals in total and at any stage increased progressively across the study period (Table 1.3). Among hospitals that responded to the AHA IT supplemental survey in 2013, 38.4 percent met any LHS criteria, increasing to 64.3 percent in 2016 and to 74.2 percent in 2018. A similar progression was seen across stages two and three. Hospitals meeting stage two LHS criteria increased from 15.8 percent in 2013 to 20.9 percent in 2016, and to 25 percent in 2018. Most notably, stage three LHS hospitals, defined as HII in this study, increased from 11.9 percent in 2013, to 30.3 percent in 2016, and to 37.2 percent of all respondent hospitals in 2018. These results suggest that a large proportion of hospitals are moving toward full LHS capability (LHS stage three).
HII Prevalence Overall and by Subcategories
Among hospitals that were MU3 compliant, 9 percent met HII criteria in 2013, increasing to 42.2 percent in 2016, and to 46.9 percent in 2018 (Table 1.4). The prevalence of HII hospitals based on the other two HII subcategorical defining criteria was relatively less definitive than for the AHA IT supplemental subcategories (the LHS and MU3). Among PCORI-funded hospitals, 2 percent met HII criteria in 2013, 1.5 percent in 2016, and 2.9 percent in 2018. MH/SN hospitals were the least represented in HII criteria, accounting for 0.7 percent in 2013 and 2016, and just 0.4 percent in 2018. HII hospitals defined by MU3 or consistency with full LHS criteria were the most prevalent across the three time periods, while hospitals in the PCORI-funded category accounted for 1-3 percent, and those in the MH/SN category accounted for less than 1 percent of all HII-defined hospitals across the study periods.
HII Subcategory Interactions
Hospitals in the four HII subcategories were cross-tabulated with each other per year of data to understand how they interacted. Results indicate that hospitals that met the full LHS criteria were more likely to also meet the criteria to be PCORI, MU3, or MH/SN subcategories (Table 1.5). MU3 subcategory hospitals were often in combination with hospitals that met full LHS criteria when compared to those that were not fully consistent with LHS criteria (e.g., stage one or two) across all three data points (23.3 percent versus 7.1 percent (2013), 75.5 percent versus 27.6 percent (2016), and 59.8 percent versus 39.3 percent (2018); (chi-sq = 110.2, p < 0.01 (2013); 719, p < 0.01; 139.6 (2016); p < 0.01 (2018)).
Similarly, hospitals in the PCORI-funded subcategory were more likely to be in combination with those that were fully consistent with the full LHS criteria when compared with those that were not PCORI funded (21.9 percent versus 11.7 percent (2013), 54.7 percent versus 29.9 percent (2016), and 67.3 percent versus 36.3 percent (2018)); (chi-sq = 6.1, p < 0.05 (2013); chi-sq = 15.3, p < 0.01 (2016); chi–sq = 40.3; p < 0.01 (2018)). Hospitals in the MH/SN subcategory were also more likely to be in combination with those that met the full LHS criteria when compared with hospitals that were not MH/SN (22.7 percent versus 11.9 percent (2013); 91.7 percent versus 29.8 percent (2016); and 92.9 percent versus 37 percent (2018); (chi-sq = 43.2; p < 0.01 (2016)); (chi-sq = 18.6; p < 0.01 (2018)).
MU3 subcategory hospitals were more in combination with hospitals that were PCORI funded when compared to hospitals that were not PCORI funded (20 percent versus 9 percent (2013); 76 percent versus 42 percent (2016); and 60 percent versus 47 percent (chi-sq = 24.4, p < 0.01 (2016); (chi-sq = 7.6, p < 0.01 (2018)). MU3-compliant hospitals were also more likely to be in combination with hospitals that were MH/SN when compared with those that were not MH/SN (13.6 percent versus 9 percent (2013); 83.3 percent versus 42 percent (2016); and 71.4 percent versus 48.6 percent (chi–sq = 1, p = 0.444 (2013); chi-sq = 16.7, p < 0.01(2016); (chi-sq = 3.4, p < 0.05 (2018). Finally, hospitals that met the criteria to be MH/SN were more likely to be in combination with those that were PCORI funded when compared with those that were non-MH/SN (13.6 percent versus 1.9 percent (2013); 4.2 percent versus 1.4 percent (2016); and 21.4 percent versus 2.8 percent (2018) (chi-sq = 15.8, p < 0.001) (2013); chi-sq = 1.2, p = 0.264 (2016) chi-sq = 17.5, p < 0.001) (2018)).
Key Hospital Subcategories and Interactions Across the Study Period
The full LHS subcategory, the MU3-compliant subcategory, or their combination were dominant across the study period (Table 1.6). By 2018, the PCORI-funded subcategory made up less than 1 percent of all HII hospitals, and no stand-alone MH/SN hospitals were observed. Less than 1 percent (0.1 percent) of hospitals met the criteria for inclusion in all four subcategories in 2018, and no hospital met the criteria for inclusion in all four subcategories in 2013 and 2016.
Discussion
In this study, HII hospitals more than tripled (19.9 percent to 62.4 percent) over the five-year study period. Among the HII hospitals, about 60 percent were consistently full LHS hospitals. Overall, full LHS and all other hospitals that met the criteria for HII hospitals grew at the same rate across the five-year period. Hospitals that met any of the LHS criteria also increased progressively, with emerging LHS hospitals increasing across the study period. By 2018, nearly three quarters (74 percent) of the hospitals evaluated met any LHS criteria (LHS stages one to three). In 2016, the meaningful use stage three law that was released in 2015 resulted in an increase in adoption, and this category became the most common among the HII subcategories (84.6 percent) (Table 1.4), and by 2018 nearly half (46.9 percent) of the hospitals evaluated had made this transition. This shows that across the study period, more hospitals met the study’s definition of HII hospitals through compliance with the HITECH Act’s MU3 criteria.
MH/SN- and PCORI-funded hospitals were generally fewer than other designations across the study period, with the proportion of hospitals meeting either set of criteria decreasing across the period. MH/SN hospitals decreased by more than one-third (36.4 percent) between 2013 and 2018, coinciding with the end of financial incentives. Among hospitals that were included in multiple categories, those that met the full LHS criteria and were also MU3 compliant were the most dominant across the study period. In 2016 and 2018, at least half of PCORI-funded or MH/SN hospitals were in combination with either hospitals that met the full LHS criteria or those that were MU3 compliant. MH/SN hospitals were, however, less likely to be in combination with PCORI-funded hospitals.
Overall, across the study period, more hospitals became HII or acquired additional HII subcategories by becoming full LHS or MU3 compliant. There were multiple drivers for this observed trend in any given hospital, all incentivizing or facilitating more capture and use of electronic health data and thus driving increases in most HII subcategories. Some of this increase might have been due to efforts to improve quality and efficiency, which led more hospitals to participate in programs such as the LHS and the potential loss of revenue due to penalties for MU3 noncompliance. Also, supplemental funding targeted toward developing patient-centric measures that are facilitated by health IT tools such as in the PCORI funded and the MH/SN subcategories might have contributed to this shift. However, when incentives for specific, narrowly focused programs such as the MH/SN end, that metric also falls, but even so, may have contributed in more lasting ways to other HII strategies.
Limitations
Study limitations include the identification, definition, and operationalization of variables, and selection of measurements and statistical tests. Adaptation of the AHA IT survey and data to create HII criteria might have led to study design and measurement errors. Also, the study evaluated MU3 compliance in 2013, although the final rule was not officially published until 2015. Given the exploratory nature of this study, there also might be other means of defining HII subcategories that were not identified.
Conclusions and Implications for Practice, Policy, and Further Research
This exploratory study showed the types, distribution, and interactions of hospitals that are more likely to be interested in improving their health IT capabilities to facilitate continuous improvement in care processes, improved patient care, and engagement using electronic health data. The adoption of LHS principles, MU3 compliance, and the availability of PCORI and CMS funds for health IT innovation have facilitated the development of health-IT driven, patient-centric hospitals. Knowledge of how widespread participation in these initiatives and programs is can help HIM professionals to understand the differential uptake of these initiatives among different HII hospitals, and this can signal the potential for synergistic approaches in health IT adoption through their combination. Identifying differences in the uptake of these initiatives and programs can also help to bridge access gaps. For example, the reduction in the MH/SN subcategory across the study period indicate that this initiative might need to be extended to achieve its health-IT-related objectives. The prevalence of the full LHS and MU3 subcategories indicate that these initiatives are spreading and are increasing the overall number of HII hospitals across the US.
Awareness of the various hospital types described in this study by HIM professionals can help to identify programs, approaches, and policies that are expanding (such as the LHS and MU3) and can enable the development of patient-centric and learning-focused HIM systems and facilitate continuous improvement processes using electronic health data. An understanding of these hospital types and their interactions can also contribute to improvements in systemic learning competencies as it relates to healthcare organizations among HIM professionals and create more avenues to be engaged in HIM decision-making within health systems. Future studies can use the developed measures of LHS practice in this study to evaluate LHS uptake in aspiring learning hospitals. Future studies can also evaluate the relationship of these HII hospitals with specific health IT indicators such as patient-generated health data capture and use.
Conflicts of Interest and Support
We have no conflicts of interest or financial support to report.
Notes
1. Thomas John Foley, Luke Vale, “What Role for Learning Health Systems in Quality Improvement within Healthcare Providers?,” Learning Health Systems 1, no. 4 (May 31, 2017): e10025, https://doi.org/10.1002/lrh2.10025.
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3. Ibid.
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7. Thomas John Foley, Luke Vale. 2017.
8. “American Recovery and Reinvestment Act of 2009: Advance Interoperable Health Information Technology Services to Support Health Information Exchange Program” (U.S. Department of Health and Human Services Office of the National Coordinator for Health Information Technology American, 2015).
9. Thomas J. Power et al., “Coordinating Systems of Care Using Health Information Technology: Development of the ADHD Care Assistant,” Advances in School Mental Health Promotion 9, no. 3-4 (2016): 201–18, https://doi.org/10.1080/1754730X.2016.1199283.
10. Daniel Gottlieb and Weinstein Scott, “Meaningful Use Stage 3 Final Rules Encourage Providers to Engage Patients in Their Health Care,” McDermott Will & Emery, November 12, 2015, https://www.mwe.com/insights/meaningful-use-stage-3-final-rules/.
11. UM, “Patient-Centered Network of Learning Health Systems (LHSNet) Approved for More than $8.6 Million to Participate in PCORnet, a Unique National Clinical Research Network | Michigan Medicine. News.,” www.uofmhealth.org, 2015, https://www.uofmhealth.org/news/archive/2https://www.uofmhealth.org/news/archive/201508/patient-centered-network-learning-health-systems-lhsnet.
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16. R. L. Fleurence et al. 2014.
17. Teresa A. Coughlin et al. 2012.
18. CMS, “Health Care Innovation Awards Round One Project Profiles.,” 2013, http://innovation.cms.gov/initiatives/Health-Care-Innovation-Awards/.
19. Katharine Witgert and Catherine Hess, “Issues and Policy Options in Sustaining a Safety Net Infrastructure to Meet the Health Care Needs of Vulnerable Populations” (National Academy For State Health Policy, August 2012).
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23. Peter Senge, The Fifth Discipline: The Art & Practice of the Learning Organization. (New York: Doubleday/Currency, 1990).
24. Ibid.
25. David Garvin, “Building a Learning Organization,” Harvard Business Review, July 1993, https://hbr.org/1993/07/building-a-learning-organization.
26. Teresa A. Coughlin et al. 2012.
27. Infed, “The Learning Organization: Principles, Theory and Practice – Infed.org”, infed.org, accessed July 5, 2022, https://infed.org/mobi/the-learning-organization.
28. Sandra Kerka, “The Learning Organization. Myths and Realities. 1995” (ERIC Clearinghouse on Adult, Career, and Vocational Education, Columbus, Ohio., 1995).
29. Ravi Reddy, “Attaining Meaningful Use of Health Information Technology in a Residency Program: Challenges and Rewards,” Hawaii J Med Public Health 71, no. 10 (2012): 287–93.
30. Gerardo Dimaguila, Kathleen Gray, and Mark Merolli, “Patient-Reported Outcome Measures of Utilizing Person-Generated Health Data in the Case of Simulated Stroke Rehabilitation: Development Method.” Journal of Medical Internet Research 22, no. 5 (2020), https://doi.org/10.2196/16827.
31. Daniel Davis, Marc Williams, and Rebecca Stametz, “Geisinger’s Effort to Realize Its Potential as a Learning Health System: A Progress Report,” Learning Health Systems 5, no. 2 (February 18, 2020), https://doi.org/10.1002/lrh2.10221.
32. Charles Friedman et al., “Toward a Science of Learning Systems: A Research Agenda for the High-Functioning Learning Health System,” Journal of the American Medical Informatics Association 22, no. 1 (October 23, 2014): 43–50, https://doi.org/10.1136/amiajnl-2014-002977.
33. IOM, Digital Infrastructure for the Learning Health System (Washington, D.C.: National Academies Press, 2011), https://doi.org/10.17226/12912.
34. C. P. Friedman, J. C. Rubin, and K. J. Sullivan, “Toward an Information Infrastructure for Global Health Improvement,” Yearbook of Medical Informatics 26, no. 1 (August 1, 2017): 16–23, https://doi.org/10.15265/IY-2017-004.
35. Rubin Joshua, “Weaving Together a Healthcare Improvement Tapestry: Learning Health System Brings Together Health IT Data Stakeholders to Share Knowledge and Improve Health,” Journal of AHIMA 85, no. 5 (2012): 38–43, https://library.ahima.org/doc?oid=300438#.YogRHJPMLdd.
36. Thomas John Foley, Luke Vale. 2017.
37. Daniel Mullins et al., “Transitioning from Learning Healthcare Systems to Learning Health Care Communities,” Journal of Comparative Effectiveness Research 7, no. 6 (June 2018): 603–14, https://doi.org/10.2217/cer-2017-0105.
38. Charles P. Friedman et al., “The Science of Learning Health Systems: Foundations for a New Journal,” Learning Health Systems 1, no. 1 (November 29, 2016): e10020, https://doi.org/10.1002/lrh2.10020.
39. AHA, “AHA Hospital Statistics,” 2018, https://www.aha.org/statistics/2016-12-27-aha-hospital-statistics-2018-edition.
40. AHA, “AHA Annual Survey Information Technology Supplement,” 2013.
41. AHA, “AHA Annual Survey Information Technology Supplement,” 2016.
42. CMS, “Health Care Innovation Awards Round One Project Profiles.” 2013.
43. FMT, “Critical Access Hospital Locations List | Flex Monitoring Team,” www.flexmonitoring.org, 2020, https://www.flexmonitoring.org/data/critical-access-hospital-locations/.
44. PCORI, “Explore Our Portfolio,” Explore Our Portfolio | PCORI, 2020, https://www.pcori.org/research-results?keywords=&#search-results.
45. Amy Finkelstein et al., “Effect of Medicaid Coverage on ED Use — Further Evidence from Oregon’s Experiment,” New England Journal of Medicine 375, no. 16 (October 20, 2016): 1505–7, https://doi.org/10.1056/nejmp1609533.
46. CMS, “Health Care Innovation Awards Round One Project Profiles,” 2013.
47. Clifton O. Bingham et al., “Using Patient-Reported Outcomes and PROMIS in Research and Clinical Applications: Experiences from the PCORI Pilot Projects,” Quality of Life Research 25, no. 8 (February 25, 2016): 2109–16, https://doi.org/10.1007/s11136-016-1246-1.
48. C. P. Friedman, J. C. Rubin, and K. J. Sullivan. 2017.
49. Rubin Joshua. 2012.
50. Matthew Menear et al., “A Framework for Value-Creating Learning Health Systems,” Health Research Policy and Systems 17, no. 1 (August 9, 2019), https://doi.org/10.1186/s12961-019-0477-3.
51. Daniel Mullins et al. 2018.
Author Biographies
Ibukun Fowe (ifowe@fsu.edu) is a postdoctoral researcher at Florida State University.
Neal Wallace (nwallace@pdx.edu) is a professor of health management and policy, and health systems and policy at the OHSU-PSU School of Public Health.
Jill Rissi (jrissi@pdx.edu) is an associate professor of health system management and policy and the director of the MPH in Health Management & Policy Program at OHSU-PSU School of Public Health.