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Quality Improvement

Quality Improvement. HIT’s Impact on a Patient Safety Culture. Lecture c.

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Quality Improvement

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  1. Quality Improvement HIT’s Impact on a Patient Safety Culture Lecture c This material (Comp 12 Unit 7) was developed by Johns Hopkins University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000013. This material was updated in 2016 by Johns Hopkins University under Award Number 90WT0005. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/.

  2. HIT’s Impact on a Patient Safety CultureLearning Objectives — Lecture c • Identify techniques for adaptive leadership. • Identify frameworks to support a patient safety culture. • Differentiate between technical and adaptive change.

  3. 2013 Medical Error Case Study • Pablo Garcia, 16, received a 39-fold overdose of a common antibiotic at a leading academic medical center. A 50-step error-prone process was identified between the doctor who wrote the order to the nurse administration.

  4. NISTIR 7804: Use Error Root Causes — 1 • Patient identification error: • Performed on one patient or documented in one patient’s record that were intended for another. • Mode error: • Actions performed in one mode intended for another. • Data accuracy error: • Displayed data are not accurate. • Data availability error: • Decisions are based upon incomplete information because related information is not updated within a reasonable amount of time or requires additional navigation, access to another provider’s note, or taking actions to update the status.

  5. NISTIR 7804: Use Error Root Causes — 2 • Interpretation error: • Differences in measurement systems, conventions, and terms contribute to erroneous assumption about meaning of information. • Recall error: • Requires users to remember information rather than to recognize it. • Feedback error: • Insufficient information because of a lack of system feedback about automated actions. • Data integrity error: • Stored data that becomes corrupted or deleted.

  6. Risk Assessment from Root Cause Analysis A model for analysis and understanding of use-related risks of EHR systems. 7.02 Figure. Lowry et al., 2012. p. 16.

  7. Classification of Human Interaction Relationship of user actions and use errors (from ANSI/AAMI/IEC 62366). 7.03 Figure. Lowry et al., 2012. p. 22.

  8. NCC MERP Index for Categorizing Medication Errors Levels of patient harm (National Coordinating Council, 2001). 7.04 Figure. Lowry et al., 2012. p. 24.

  9. Institute of Medicine Report on Health IT and Patient Safety • Features of a safe health IT system: • Easy retrieval of accurate, timely, and reliable data. • A system the user wants to interact with. • Simple and intuitive data displays. • Easy navigation. • Evidence at the point of care to aid decision making. • Enhancements to workflow, automating mundane tasks, and streamlining work, never increasing physical or cognitive workload. • Easy transfer of information to and from other organizations and providers. • No unanticipated downtime.

  10. Technical vs. Adaptive Change 7.05 Table.

  11. HIT’s Impact on a Patient Safety CultureSummary — Lecture c • Health IT has great potential to reduce medical errors. • It also has the potential to introduce new opportunities for medical errors and overreliance on technology.

  12. HIT’s Impact on a Patient Safety CultureReferences — Lecture c — 1 References Institute of Medicine, Committee on Patient Safety and Health Information Technology, Board on Health Care Services. (2012). Health IT and patient safety: Building safer systems for better care. Washington, DC: National Academies Press. Lowry, S. Z., Quinn, M.T., Ramaiah, M., Schumacher, R. M., Patterson, E. S., North, R., et al. (2012). Technical evaluation, testing, and validation of the usability of electronic health records (NISTIR 7804). Washington, DC: National Institute of Standards and Technology, U.S. Department of Commerce. Retrieved May 25, 2016, from http://www.nist.gov/healthcare/usability/upload/EUP_WERB_Version_2_23_12-Final-2.pdf Wachter, R. (2015). The overdose: Harm in a wired hospital: How medical tech gave a patient a massive overdose. In Wachter, R. (2015). The digital doctor: Hope, hype, and harm at the dawn of medicine’s computer age. New York: McGraw-Hill. Backchannel. Retrieved May 25, 2016, from https://backchannel.com/how-technology-led-a-hospital-to-give-a-patient-38-times-his-dosage-ded7b3688558#.xux4bfy5v

  13. HIT’s Impact on a Patient Safety CultureReferences — Lecture c — 2 Charts, Tables, Figures 7.02 Figure: A Model for Analysis and Understanding of Use-related Risks of EHR systems [Screen shot]. Lowry, S. Z., Quinn, M.T., Ramaiah, M., Schumacher, R. M., Patterson, E. S., North, R., et al. (2012). Technical evaluation, testing, and validation of the usability of electronic health records (NISTIR 7804). p. 16. Washington, DC: National Institute of Standards and Technology, U.S. Department of Commerce. Retrieved May 25, 2016, from http://www.nist.gov/healthcare/usability/upload/EUP_WERB_Version_2_23_12-Final-2.pdf 7.03 Figure: Relationship of user actions and use errors (from ANSI/AAMI/IEC 62366) [Screen shot]. Lowry, S. Z., Quinn, M.T., Ramaiah, M., Schumacher, R. M., Patterson, E. S., North, R., et al. (2012). Technical evaluation, testing, and validation of the usability of electronic health records (NISTIR 7804). p. 22. Washington, DC: National Institute of Standards and Technology, U.S. Department of Commerce. Retrieved May 25, 2016, from http://www.nist.gov/healthcare/usability/upload/EUP_WERB_Version_2_23_12-Final-2.pdf

  14. HIT’s Impact on a Patient Safety CultureReferences — Lecture c — 3 Charts, Tables, Figures 7.04 Figure: Levels of Patient Harm (National Coordinating Council, 2001) [Screen shot]. Lowry, S. Z., Quinn, M.T., Ramaiah, M., Schumacher, R. M., Patterson, E. S., North, R., et al. (2012). Technical evaluation, testing, and validation of the usability of electronic health records (NISTIR 7804). p. 24. Washington, DC: National Institute of Standards and Technology, U.S. Department of Commerce. Retrieved May 25, 2016, from http://www.nist.gov/healthcare/usability/upload/EUP_WERB_Version_2_23_12-Final-2.pdf 7.05 Table: Technical vs. Adaptive Change. Johns Hopkins University. (2016).

  15. Quality ImprovementHIT’s Impact on a Patient Safety CultureLecture b This material (Comp 12 Unit 7) was developed by Johns Hopkins University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000013. This material was updated by Johns Hopkins University under Award Number 90WT0005.

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