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Healthcare Information Management Systems pp 493–514Cite as

Beyond Current HIMS: Future Visions and a Roadmap

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Part of the book series: Health Informatics ((HI))

Abstract

This chapter explores the new technologies being developed to support clinical decision-making and care delivery and presents a roadmap for the safer and more effective next generation health information management systems. Innovative applications (apps) are being built today using twenty-first century web technologies and cloud-based architectural platforms. A basic premise for these new care delivery support systems is that they are built as vendor-agnostic, patient-centric apps and use the current electronic medical records (EHR) and/or clinical systems as infrastructure. The roadmap presented here projects that these new clinical decision support (CDS) and care delivery apps will be built upon but outside of the current electronic health record (EHR) systems – referred to here under the general term of health information management systems (HIMS). Current HIMS will play a valuable role in the data collection process. However, it must acknowledged that the data collected by today’s HIMS systems must be augmented with data not now routinely captured. The challenge is not to undo the mainstream HIMSs but rather to use the data they record, augment the data as is possible and build apps (including novel visualizations) on the vendor-agnostic patient-centric data. A self-perpetuating cycle must be created so that as more apps are built, users (clinicians and consumers) will clamor for more. As more apps are used they will become more refined. As this cycle spins, there can be that optimism the ultimate goal – improved patient care – will be realized.

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References

  1. Middleton B, Bloomrosen M, Dente MA, et al. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. JAMIA. 2013;20(e1):e2–8.

    PubMed Central  PubMed  Google Scholar 

  2. Institute of Medicine. Health IT and patient safety: building safer systems for better care. Washington, DC: The National Academies Press; 2012. p. 1–235.

    Google Scholar 

  3. Gordon JE, Leiman JM, Deland EL, Pardes H. Delivering value: provider efforts to improve the quality and reduce the cost of health care. Annu Rev Med. 2014;65(1):447–58.

    Article  CAS  PubMed  Google Scholar 

  4. Garg AX, Adhikari NKJ, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223–38.

    Article  CAS  PubMed  Google Scholar 

  5. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.

    Article  PubMed Central  PubMed  Google Scholar 

  6. Kurzweil R. Human 2.0. New Sci. 2005;187(2518):32–7.

    PubMed  Google Scholar 

  7. Fox J, Glasspool D, Patkar V, et al. Delivering clinical decision support services: there is nothing as practical as a good theory. J Biomed Inform. 2010;43(5):831–43.

    Article  PubMed  Google Scholar 

  8. Darst JR, Newburger JW, Resch S, Rathod RH, Lock JE. Deciding without data. Congenit Heart Dis. 2010;5(4):339–42.

    Article  PubMed Central  PubMed  Google Scholar 

  9. Kahneman D. Thinking, fast and slow. New York: Macmillan; 2011.

    Google Scholar 

  10. Klein G. Sources of power: how people make decisions. Cambridge: MIT Press; 1999.

    Google Scholar 

  11. Fackler JC, Watts C, Grome A, Miller T, Crandall B, Pronovost P. Critical care physician cognitive task analysis: an exploratory study. Crit Care. 2009;13(2):R33.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Custer JW, White E, Fackler JC, et al. A qualitative study of expert and team cognition on complex patients in the pediatric intensive care unit. Pediatr Crit Care Med. 2012;13(3):278–84.

    Article  PubMed  Google Scholar 

  13. Shirts BH, Bennett ST, Jackson BR. Using patients like My patient for clinical decision support: institution-specific probability of celiac disease diagnosis using simplified near-neighbor classification. J Gen Intern Med. 2013;28(12):1565–72.

    Article  PubMed Central  PubMed  Google Scholar 

  14. Amancio DR, Comin CH, Casanova D, et al. A systematic comparison of supervised classifiers. PLoS ONE. 2014;9(4):e94137. Shen H-B, ed.

    Article  PubMed Central  PubMed  Google Scholar 

  15. Kahneman D, Klein G. Conditions for intuitive expertise. Am Psychol. 2009;64(6):515–51526.

    Article  PubMed  Google Scholar 

  16. Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science. 1974;185(4157):1124–31.

    Article  CAS  PubMed  Google Scholar 

  17. Dawson NV, Arkes HR. Systematic errors in medical decision making: judgment limitations. J Gen Intern Med. 1987;2(3):183–7.

    Article  CAS  PubMed  Google Scholar 

  18. Aberegg SK. Omission bias and decision making in pulmonary and critical care medicine. Chest. 2005;128(3):1497–505.

    Article  PubMed  Google Scholar 

  19. Berner ES, Graber ML. Overconfidence as a cause of diagnostic error in medicine. Am J Med. 2008;121(5 Suppl):S2–23.

    Article  PubMed  Google Scholar 

  20. Croskerry P, Singhal G, Mamede S. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf. 2013;22 Suppl 2:ii58–64.

    PubMed Central  PubMed  Google Scholar 

  21. Kahneman D, Lovallo D, Sibony O. Before you make that big decision. Harv Bus Rev. 2011;89(6):50–60. 137.

    PubMed  Google Scholar 

  22. di Martino P, Leoli F, Cinotti F, et al. Improving vital sign documentation at triage: an emergency department quality improvement project. J Patient Saf. 2011;7(1):26–9.

    Article  PubMed  Google Scholar 

  23. Bilyeu P, Eastes L. Use of the electronic medical record for trauma resuscitations. J Trauma Nurs. 2013;20(3):166–8.

    Article  PubMed  Google Scholar 

  24. Medical Device Data Systems, Medical Image Storage Devices, and Medical Image Communications Devices. 2015. Available at: http://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/GeneralHospitalDevicesandSupplies/MedicalDeviceDataSystems/default.htm. Accessed 22 Mar 2015.

  25. Seely AJ, Bravi A, Herry C, et al. Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients? Crit Care. 2014;18(2):R65.

    Article  PubMed Central  PubMed  Google Scholar 

  26. Park S, Kaffashi F, Loparo KA, Jacono FJ. The use of heart rate variability for the early detection of treatable complications after aneurysmal subarachnoid hemorrhage. J Clin Monit Comput. 2013;27(4):385–93.

    Article  PubMed  Google Scholar 

  27. Moorman JR, Waldemar AC, Kattwinkel J, et al. Mortality reduction by heart rate characteristic monitoring in very low birth weight neonates: a randomized trial. J Pediatr. 2011;159(6):900-6.e1.

    Article  PubMed  Google Scholar 

  28. Hug CW, Clifford GD. An analysis of the errors in recorded heart rate and blood pressure in the ICU using a complex set of signal quality metrics. Comput Cardiol. 2007;34:641–4.

    Google Scholar 

  29. Hug CW, Clifford GD, Reisner AT. Clinician blood pressure documentation of stable intensive care patients: an intelligent archiving agent has a higher association with future hypotension. Crit Care Med. 2011;39(5):1006–14.

    Article  PubMed Central  PubMed  Google Scholar 

  30. FDASIA Health IT Report. 2014:1–34. http://www.fda.gov/downloads/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDRH/CDRHReports/UCM391521.pdf. Accessed 23 Mar 2015.

  31. Karnik K. FDA regulation of clinical decision support software. J Law Biosci. 2014;1(2):202–8.

    Article  Google Scholar 

  32. Goldman JM. Solving the interoperability challenge: safe and reliable information exchange requires more from product designers. IEEE Pulse. 2014;5(6):37–9.

    Article  PubMed  Google Scholar 

  33. Arney D, Goldman JM, Bhargav-Spantzel A, et al. Simulation of medical device network performance and requirements for an integrated clinical environment. Biomed Instrum Technol. 2012;46(4):308–15.

    Article  PubMed  Google Scholar 

  34. Yim S-H, Chung Y-J. Reflections on the US FDA’s warning on direct-to-consumer genetic testing. Genom Inform. 2014;12(4):151–5.

    Article  Google Scholar 

  35. Carere DA, Couper MP, Crawford SD, et al. Design, methods, and participant characteristics of the Impact of Personal Genomics (PGen) Study, a prospective cohort study of direct-to-consumer personal genomic testing customers. Genome Med. 2014;6(12):96.

    Article  PubMed Central  PubMed  Google Scholar 

  36. Swan M. Health 2050: the realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. JPM. 2012;2(4):93–118.

    Article  PubMed Central  PubMed  Google Scholar 

  37. Caulfield B, Kaljo I, Donnelly S. Use of a consumer market activity monitoring and feedback device improves exercise capacity and activity levels in COPD. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:1765–8.

    PubMed  Google Scholar 

  38. McCambridge J, Witton J, Elbourne DR. Systematic review of the Hawthorne effect: new concepts are needed to study research participation effects. J Clin Epidemiol. 2014;67(3):267–77.

    Article  PubMed Central  PubMed  Google Scholar 

  39. Available at: https://www.patientslikeme.com. Accessed 30 Mar 2015.

  40. Frost JH, Massagli MP, Wicks P, Heywood J. How the social Web supports patient experimentation with a new therapy: the demand for patient-controlled and patient-centered informatics. AMIA Annu Symp Proc. 2007;6:217–21.

    Google Scholar 

  41. Fornai F, Longone P, Cafaro L, et al. Lithium delays progression of amyotrophic lateral sclerosis. Proc Natl Acad Sci U S A. 2008;105(6):2052–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Wicks P, Vaughan TE, Massagli MP, Heywood J. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nat Biotechnol. 2011;29(5):411–4.

    Article  CAS  PubMed  Google Scholar 

  43. UKMND-LiCALS Study Group. Lithium in patients with amyotrophic lateral sclerosis (LiCALS). Lancet Neurol. 2013;12(4):339–45.

    Article  PubMed Central  Google Scholar 

  44. Chiò A, Mora G. The final chapter of the ALS lithium saga. Lancet Neurol. 2013;12(4):324–5.

    Article  PubMed  Google Scholar 

  45. Wicks P, Vaughan T, Heywood J. Subjects no more: what happens when trial participants realize they hold the power? BMJ. 2014;348(jan28 9):g368.

    Article  PubMed Central  PubMed  Google Scholar 

  46. Mandl KD, Kohane IS. No small change for the health information economy. N Engl J Med. 2009;360(13):1278–81.

    Article  CAS  PubMed  Google Scholar 

  47. Mandl KD, Kohane IS. Escaping the EHR trap – the future of health IT. N Engl J Med. 2012;366(24):2240–2.

    Article  CAS  PubMed  Google Scholar 

  48. Mandl KD, Mandel JC, Murphy SN, et al. The SMART Platform: early experience enabling substitutable applications for electronic health records. J Am Med Inform Assoc. 2012;19(4):597–603.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Introducing HL7 FHIR®. hlorg. 2014:1–2. http://hl7.org/implement/standards/fhir/fhir-summary.pdf. Accessed 29 Mar 2015.

  50. Bosl W, Mandel J, Jonikas M, Ramoni RB, Kohane IS, Mandl KD. Scalable decision support at the point of care: a substitutable electronic health record App for monitoring medication adherence. Interact J Med Res. 2013;2(2):e13.

    Article  PubMed Central  PubMed  Google Scholar 

  51. Klann JG, McCoy AB, Wright A, Wattanasin N, Sittig DF, Murphy SN. Health care transformation through collaboration on open-source informatics projects: integrating a medical applications platform, research data repository, and patient summarization. Interact J Med Res. 2013;2(1):e11–8.

    Article  PubMed Central  PubMed  Google Scholar 

  52. Croskerry P. From mindless to mindful practice – cognitive bias and clinical decision making. N Engl J Med. 2013;368(26):2445–8.

    Article  CAS  PubMed  Google Scholar 

  53. Hough D. Irrationality in health care: what behavioral economics reveals about what we do and why. Stanford: Stanford Economics and Finance, Stanford University Press; 2013.

    Google Scholar 

  54. Barnett GO, Cimino JJ, Hupp JA, Hoffer EP. DXplain. An evolving diagnostic decision-support system. JAMA. 1987;258(1):67–74.

    Article  CAS  PubMed  Google Scholar 

  55. Bond WF, Schwartz LM, Weaver KR, Levick D, Giuliano M, Graber ML. Differential diagnosis generators: an evaluation of currently available computer programs. J Gen Intern Med. 2011;27(2):213–9.

    Article  PubMed Central  Google Scholar 

  56. Henderson EJ, Rubin GP. The utility of an online diagnostic decision support system (Isabel) in general practice: a process evaluation. JRSM Short Rep. 2013;4(5):31.

    Article  PubMed Central  PubMed  Google Scholar 

  57. Fackler JC, Wetzel RC. Critical care for rare diseases. Pediatr Crit Care Med. 2001;3(1):89–90.

    Article  Google Scholar 

  58. Fackler J, Lehmann HP, Wetzel RC. Critical care for rare diseases (and procedures). Pediatr Crit Care Med. 2015;16(3):297–9.

    Article  PubMed  Google Scholar 

  59. Cifra CL, Jones KL, Ascenzi JA, Bhalala US, Bembea MM, Newman-Toker DE, Fackler JC, Miller MR. Diagnostic errors in a PICU. Pediatr Crit Care Med. 2015;16(5):468–76.

    Article  PubMed  Google Scholar 

  60. Doyle-Lindrud S. Watson will see you now: a supercomputer to help clinicians make informed treatment decisions. CJON. 2015;19(1):31–2.

    Article  Google Scholar 

  61. Carey B. Learning to see data. NY Times. 2015;164(56,820):SR1.

    Google Scholar 

  62. Worth a thousand words. The economist. 2007. Available at: http://www.economist.com/node/10278643.

  63. Wurman RS. Understanding healthcare. Newport: Top; 2004.

    Google Scholar 

  64. Holzinger A. Biomedical informatics. New York: Springer International Publishing; 2014. doi:10.1007/978-3-319-04528-3.

    Book  Google Scholar 

  65. Rind A. Interactive information visualization to explore and query electronic health records. FNT Human Comput Interact. 2013;5(3):207–98.

    Article  Google Scholar 

  66. Wenkebach U, Pollwein B, Finsterer U. Visualization of large datasets in intensive care. Proc Annu Symp Comput Appl Med Care. 1992:18–22.

    Google Scholar 

  67. Powsner SM, Tufte ER. Graphical summary of patient status. Lancet. 1994;344(8919):386–9.

    Article  CAS  PubMed  Google Scholar 

  68. Christensen CM, Bohmer R, Kenagy J. Will disruptive innovations cure health care? Harv Bus Rev. 2000;78(5):102–12. 199.

    CAS  PubMed  Google Scholar 

  69. Hwang J, Christensen CM. Disruptive innovation in health care delivery: a framework for business-model innovation. Health Aff (Millwood). 2007;27(5):1329–35.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Pediatric Anesthesiology and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    James Fackler MD

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  1. James Fackler MD
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Correspondence to James Fackler MD .

Editor information

Editors and Affiliations

  1. Gentiva Health Services, Atlanta, Georgia, USA

    Charlotte A. Weaver

  2. Healthcare Informatics, IBM Research, Baltimore, Maryland, USA

    Marion J. Ball

  3. Division of Health Sciences Informatics, The Johns Hopkins University, Baltimore, Maryland, USA

    George R. Kim

  4. Duquesne University, Pittsburgh, Pennsylvania, USA

    Joan M. Kiel

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© 2016 Springer International Publishing Switzerland

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Fackler, J. (2016). Beyond Current HIMS: Future Visions and a Roadmap. In: Weaver, C., Ball, M., Kim, G., Kiel, J. (eds) Healthcare Information Management Systems. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-20765-0_29

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  • DOI: https://doi.org/10.1007/978-3-319-20765-0_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20764-3

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