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Role of Informatics in Bridging Public and Population Health

Part of the Health Informatics book series (HI)

Abstract

Population health is an evolving concept of healthcare. Population health aims to improve the health outcomes of defined populations by modifying health determinants that range from clinical to social and environmental factors. Both population and public health efforts aim to reach similar outcomes and target comparable determinants of health. Within healthcare operations, however, population health is driven by stratifying patients into groups of individuals with similar risks of undesired outcomes who will receive different types of interventions.

Informatics can potentially play a critical role in bridging the gap between population and public health efforts in a community. Sharing data on underlying determinants of health across public and private health systems is now possible due to recent advancements in health information systems. Although several statewide provider/payer-based population health programs that promote active collaborations with public health agencies through informatics exist, these efforts are still nascent.

This chapter examines the evolving concept of population health and how informatics can bridge the gap between population and public health. The chapter profiles two population health programs that leverage informatics in building what might be a model for other communities. The chapter also discusses the barriers and challenges to deploying informatics solutions to strengthen collaboration between population and public health efforts.

Keywords

  • Population health
  • Population health informatics
  • Risk stratification
  • Health determinants
  • Health outcomes

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Notes

  1. 1.

    Diagram partially adapted from “Shortliffe & Cimino. Biomedical Informatics: Computer Applications in Health Care and Biomedicine 4th edition; chapter 1, page 28; Springer-Verlag London 2014”

References

  1. Kindig DA. Understanding population health terminology. Milbank Q. 2007;85(1):139–61.

    PubMed  PubMed Central  Google Scholar 

  2. Kindig D. Improving population health policy: What is population health? Available from http://www.improvingpopulationhealth.org/blog/what-is-population-health.html. Accessed 29 Nov 2019.

  3. Kindig D. Purchasing population health: paying for results. Ann Arbor: University of Michigan Press; 1997.

    Google Scholar 

  4. Young T. Population health concepts and methods. New York: Oxford University Press; 1998.

    Google Scholar 

  5. Dunn JR, Hayes MV. Toward a lexicon of population health. Can J Public Health. 1999;90(Suppl 1):S7–S10.

    PubMed  PubMed Central  Google Scholar 

  6. Kindig D, Stoddart G. What is population health? Am J Public Health. 2003;93(3):380–3.

    PubMed  PubMed Central  Google Scholar 

  7. Kharrazi H, Lasser EC, Yasnoff WA, Loonsk J, Advani A, Lehmann HP, et al. A proposed national research and development agenda for population health informatics: summary recommendations from a national expert workshop. J Am Med Inform Assoc. 2017;24(1):2–12.

    PubMed  Google Scholar 

  8. Hatef E, Lasser EC, Kharrazi HH, Perman C, Montgomery R, Weiner JP. A population health measurement framework: evidence-based metrics for assessing community-level population health in the global budget context. Popul Health Manag. 2018;21(4):261–70.

    PubMed  Google Scholar 

  9. Hatef E, Predmore Z, Lasser E, Kharrazi H, Nelson K, Sylling P, et al. Integrating social and behavioral determinants of health into patient care and population health at veterans health administration: a conceptual framework and an assessment of available individual and population level data sources and evidence-based measu. AIMS Public Health. 2019;6(3):209–24.

    PubMed  PubMed Central  Google Scholar 

  10. Kindig DA, Asada Y, Booske B. A population health framework for setting national and state health goals. JAMA. 2008;299(17):2081–3.

    CAS  PubMed  Google Scholar 

  11. Kindig DA, Isham G. Population health improvement: a community health business model that engages partners in all sectors. Front Health Serv Manag. 2014;30(4):3–20.

    Google Scholar 

  12. Sharfstein JM, Stuart EA, Antos J. Global budgets in Maryland: assessing results to date. JAMA. 2018;319(24):2475–6.

    PubMed  Google Scholar 

  13. Hatef E, Kharrazi H, VanBaak E, Falcone M, Ferris L, Mertz K, et al. A state-wide health it infrastructure for population health: building a community-wide electronic platform for Maryland’s all-payer global budget. Online J Public Health Inform. 2017;9(3):e195.

    PubMed  PubMed Central  Google Scholar 

  14. Turnock B. Public health: what it is and how it works. Boston: Jones and Bartlett; 2004.

    Google Scholar 

  15. Last J. A dictionary of epidemiology. 4th ed. New York: Oxford University Press; 2001.

    Google Scholar 

  16. Gold MR, Stevenson D, Fryback DG. HALYS and QALYS and DALYS, Oh my: similarities and differences in summary measures of population health. Annu Rev Public Health. 2002;23:115–34.

    PubMed  Google Scholar 

  17. Kawachi I, Subramanian SV, Almeida-Filho N. A glossary for health inequalities. J Epidemiol Community Health. 2002;56(9):647–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Susser M. Glossary: causality in public health science. J Epidemiol Community Health. 2001;55(6):376–8.

    PubMed Central  Google Scholar 

  19. Kharrazi H, Chi W, Chang HY, Richards TM, Gallagher JM, Knudson SM, et al. Comparing population-based risk-stratification model performance using demographic, diagnosis and medication data extracted from outpatient electronic. Med Care. 2017;55(8):789–96.

    PubMed  Google Scholar 

  20. Kan HJ, Kharrazi H, Chang HY, Bodycombe D, Lemke K, Weiner JP. Exploring the use of machine learning for risk adjustment: a comparison of standard and penalized linear regression models in predicting health care costs. PLoS One. 2019;14(3):e0213258.

    PubMed  PubMed Central  Google Scholar 

  21. Centers for Medicare and Medicaid Services (CMS). Hospital inpatient value-based purchasing program (Medicare program; final rule). Fed Regist. 2011;76(88):26490–547.

    Google Scholar 

  22. Institute For Healthcare Improvement. The IHI triple aim. 2015. Available from http://www.ihi.org/Engage/Initiatives/TripleAim/Pages/default.aspx. Accessed 29 Nov 2019.

  23. Sharfstein JM. The strange journey of population health. Milbank Q. 2015;92(4):640–3.

    Google Scholar 

  24. Institute of Medicine. The future of the public’s health in the 21st century. Washington: The National Academies Press; 2002.

    Google Scholar 

  25. Robert Wood Johnson Foundation. Data across sectors for health. 2019. Available from https://dashconnect.org/. Accessed 29 Nov 2019.

  26. Stoto M, Davis M, Atkins A. Beyond CHNAS: performance measurement for community health improvement. EGEMS. 2019;7(1):45.

    PubMed  PubMed Central  Google Scholar 

  27. DeSalvo KB, Wang YC, Harris A, Auerbach J, Koo D, O’Carroll P. Public health 3.0: a call to action for public health to meet the challenges of the 21st century. Prev Chronic Dis. 2017;14:E78.

    PubMed  PubMed Central  Google Scholar 

  28. Gamache R, Kharrazi H, Weiner JP. Public and population health informatics: the bridging of big data to benefit communities. Yearb Med Inform. 2018;27(1):199–206.

    PubMed  PubMed Central  Google Scholar 

  29. Kharrazi H, Weiner JP. IT-enabled community health interventions: challenges, opportunities, and future directions. EGEMS. 2014;2(3):1117.

    PubMed  PubMed Central  Google Scholar 

  30. Dixon BE, Pina J, Kharrazi H, Gharghabi F, Richards J. What’s past is prologue: a scoping review of recent public health and global health informatics literature. Online J Public Health Inform. 2015;7(2):e216.

    PubMed  PubMed Central  Google Scholar 

  31. Eisenberg M, Saloner B, Krawczyk N, Ferris L, Schneider K, Lyons B, et al. Use of opioid overdose deaths reported in one state’s criminal justice, hospital, and prescription databases to identify risk of opioid fatalities. JAMA Intern Med. 2019;179(7):980–2.

    PubMed  PubMed Central  Google Scholar 

  32. Ash AS, Mick EO, Ellis RP, Kiefe CI, Allison JJ, Clark MA. Social determinants of health in managed care payment formulas. JAMA Intern Med. 2017;177(10):1424–30.

    PubMed  PubMed Central  Google Scholar 

  33. Patel A, Rajkumar R, Colmers JM, Kinzer D, Conway PH, Sharfstein JM. Maryland’s global hospital budgets--preliminary results from an all-payer model. N Engl J Med. 2015;373(20):1899–901.

    PubMed  Google Scholar 

  34. Gottlieb L, Fichtenberg C, Alderwick H, Adler N. Social determinants of health: what’s a healthcare system to do? J Healthc Manag. 2019;64(4):243–57.

    PubMed  Google Scholar 

  35. Gold R, Bunce A, Cowburn S, Dambrun K, Dearing M, Middendorf M, et al. Adoption of social determinants of health EHR tools by community health centers. Ann Fam Med. 2018;16(5):399–407.

    PubMed  PubMed Central  Google Scholar 

  36. Gottlieb LM, Francis DE, Beck AF. Uses and misuses of patient- and neighborhood-level social determinants of health data. Perm J. 2018;22:18–078.

    PubMed  PubMed Central  Google Scholar 

  37. Maryland Department of Health. Health Enterprise Zones (HEZs). 2015. Available from https://health.maryland.gov/healthenterprisezones/Pages/home.aspx. Accessed 29 Nov 2019.

  38. United States. The health insurance portability and accountability act (HIPAA). Washington, DC: US Department of Labor, Employee Benefits Security Administration; 2004.

    Google Scholar 

  39. United States. Family educational rights and privacy act of 1974. Washington, DC: US Department of Education; 1974.

    Google Scholar 

  40. Adler-Milstein J, Embi PJ, Middleton B, Sarkar IN, Smith J. Crossing the health IT chasm: considerations and policy recommendations to overcome current challenges and enable value-based care. J Am Med Inform Assoc. 2017;24(5):1036–43.

    PubMed  Google Scholar 

  41. Kharrazi H, Gonzalez CP, Lowe KB, Huerta TR, Ford EW. Forecasting the maturation of electronic health record functions among US hospitals: retrospective analysis and predictive model. J Med Internet Res. 2018;20(8):e10458.

    PubMed  PubMed Central  Google Scholar 

  42. Perlman S, McVeigh K, Thorpe L, Jacobson L, Greene C, Gwynn R. Innovations in population health surveillance: using electronic health records for chronic disease surveillance. Am J Public Health. 2017;107(6):853–7.

    PubMed  PubMed Central  Google Scholar 

  43. Hatef E, Weiner JP, Kharrazi H. A public health perspective on using electronic health records to address social determinants of health: The potential for a national system of local community. Int J Med Inform. 2019;124:86–9.

    PubMed  Google Scholar 

  44. Hatef E, Rouhizadeh M, Tia I, Lasser E, Hill-Briggs F, Marsteller J, et al. Assessing the availability of data on social and behavioral determinants in structured and unstructured electronic health records: a retrospective analysis. JMIR Med Inform. 2019;7(3):e13802.

    PubMed  PubMed Central  Google Scholar 

  45. Hatef E, Searle KM, Predmore Z, Lasser EC, Kharrazi H, Nelson K, et al. The impact of social determinants of health on hospitalization in the veterans health administration. Am J Prev Med. 2019;56(6):811–8.

    PubMed  Google Scholar 

  46. Kharrazi H, Horrocks D, Weiner J. Use of HIEs for value-based care delivery: a case study of Maryland’s HIE. In: Dixon B, editor. Health information exchange: navigating and managing a network of health information systems. Cambridge: Academic Press Elsevier; 2016. p. 313–29.

    Google Scholar 

  47. LOINC. Social determinants of health. 2019. Available from https://loinc.org/sdh/. Accessed 29 Nov 2019.

  48. Health Level 7 International. HL7 gravity project. 2019. Available from https://www.hl7.org/gravity/. Accessed 29 Nov 2019.

  49. Husain A, Sridharma S, Baker M, Kharrazi H. Incidence and geographic distribution of injuries due to falls among pediatric communities of Maryland. Pediatr Emerg Care. 2019;18:52.

    Google Scholar 

  50. Karr A, Taylor M, West S, Setoguchi S, Kou T, Gerhard T, et al. Comparing record linkage software programs and algorithms using real-world data. PLoS One. 2019;14(9):e0221459.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. OHDSI. Observational health data sciences and informatics. 2019. Available from https://ohdsi.org/. Accessed 29 Nov 2019.

  52. Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 2014;33(7):1123–31.

    Google Scholar 

  53. Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013;20(1):144–51.

    PubMed  PubMed Central  Google Scholar 

  54. Kharrazi H, Wang C, Scharfstein D. Prospective EHR-based clinical trials: the challenge of missing data. J Gen Intern Med. 2014;29(7):976–8.

    PubMed  PubMed Central  Google Scholar 

  55. Chang HY, Richards TM, Shermock KM, Elder Dalpoas DS, Kan KH, Alexander GC, et al. Evaluating the impact of prescription fill rates on risk stratification model performance. Med Care. 2017;55(12):1052–60.

    PubMed  Google Scholar 

  56. Lemke KW, Gudzune KA, Kharrazi H, Weiner JP. Assessing markers from ambulatory laboratory tests for predicting high-risk patients. Am J Manag Care. 2018;24(6):e190–5.

    PubMed  Google Scholar 

  57. Kharrazi H, Chang HY, Heins SE, Weiner JP, Gudzune KA. Assessing the impact of body mass index information on the performance of risk adjustment models in predicting health care costs and utilization. Med Care. 2018;56(12):1042–50.

    PubMed  PubMed Central  Google Scholar 

  58. Kharrazi H, Lehmann H. Role of population health informatics in understanding data, information and knowledge. In: Joshi A, Thorpe L, Waldron L, editors. Population health informatics: driving evidence-based solutions into practice. Burlington: Jones and Bartlett Learning; 2017. p. 61–86.

    Google Scholar 

  59. Wu AW, Kharrazi H, Boulware LE, Snyder CF. Measure once, cut twice--adding patient-reported outcome measures to the electronic health record for comparative effectiveness research. J Clin Epidemiol. 2013;66(8 Suppl):S12–20.

    PubMed  PubMed Central  Google Scholar 

  60. Barrett MA, Humblet O, Hiatt RA, Adler NE. Big data and disease prevention: from quantified self to quantified communities. Big Data. 2013;1(3):168–75.

    PubMed  Google Scholar 

  61. The Office of the National Coordinator for Health Information Technology. Connecting health and care for the nation: a shared nationwide interoperability roadmap. 2017. Available from https://www.healthit.gov/sites/default/files/hie-interoperability/nationwide-interoperability-roadmap-final-version-1.0.pdf. Accessed 29 Nov 2019.

  62. Assistant Secretary for Planning. Measurement of interoperable electronic health care records utilization. Washington, DC: Clinovation; 2016.

    Google Scholar 

  63. Center for Medicare and Medicaid Services (CMS). Maryland total cost of care model. 2019. Available from https://innovation.cms.gov/initiatives/md-tccm/. Accessed 29 Nov 2019.

  64. Diez Roux RA, Mujahid MS, Hirsch JA, Moore K, Moore LV. The impact of neighborhoods on CV risk. Glob Heart. 2016;11(3):353–63.

    PubMed  PubMed Central  Google Scholar 

  65. Diez Roux RA, Merkin SS, Arnett D, Chambless L, Massing M, Nieto FJ, et al. Neighborhood of residence and incidence of coronary heart disease. N Engl J Med. 2001;345(2):99–106.

    CAS  PubMed  Google Scholar 

  66. Alderwick H, Hood-Ronick CM, Gottlieb LM. Medicaid investments to address social needs in Oregon and California. Health Aff. 2019;38(5):774–81.

    Google Scholar 

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Kharrazi, H., Gamache, R., Weiner, J. (2020). Role of Informatics in Bridging Public and Population Health. In: Magnuson, J., Dixon, B. (eds) Public Health Informatics and Information Systems . Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-41215-9_5

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