Bridging the Gap between Work-as-Imagined and Work-as-Done

Ellen S Deutsch, MD, MS, FACS, FAAP, CPPS
Editor, Pennsylvania Patient Safety Advisory
Medical Director, Pennsylvania Patient Safety Authority

Work-as-Imagined and Work-as-Done

To improve the safety and quality of healthcare, we try to understand and improve how healthcare providers accomplish patient care "work." This work includes synthesizing information from a patient's history and physical examination or from a handoff; performing tests or procedures; administering medications; and providing information so that patients can make the best choices for themselves. Sometimes this work flows very well and everyone is pleased with the results. Sometimes this work does not unfold in the way that was anticipated. Perhaps the patient's condition is more complicated than usual, or perhaps a needed resource—a medication, a piece of equipment, available operating room time, or a consultant—is not readily available. Perhaps there is time pressure, or we encounter distractions and interruptions. Healthcare providers often complete tasks that are necessary for patient care despite obstacles in their path, and without necessarily reporting, let alone fixing, those obstacles.

Efforts to improve healthcare work will not succeed without recognizing that there is a difference between a theoretical construct of "work-as-imagined" and the reality of "work-as-done" (see Figure). Work-as-imagined is the illusory ideal state. Hollnagel describes work-as-imagined as what designers, managers, regulators, and authorities believe happens or should happen, which becomes the basis for design, training, and control. In contrast, work-as-done is what truly occurs and what people actually do during patient care.¹

Although a complete and perfect understanding of work-as-done is a worthy goal, healthcare delivery is a complex adaptive system that is in constant evolution with fluid, dynamic changes.^2-6 Complete understanding is an unattainable ideal. Work-as-imagined provides information based on conceptual processes; it can offer a valuable hypothetical construct of the work in question, and may be used to develop theoretical concepts and generalizable guidance. Work-as-imagined may not reflect actual conditions that impact patient care at the "sharp end," the point in patient care that directly impacts patients. However, exploring the gap between work-as-imagined and work-as-done does afford opportunities to look at work through a variety of lenses, each of which provides complementary information. Each lens has attributes and limitations; a preliminary exploration of several potential lenses, such as "work-as-documented" and "work-as-observed," follows.

With the blossoming of computer science, discrete event simulation can be used to analyze patient flow, predict demands for services, and mathematically model the impact of interventions on patient care processes. Standardized parameters for process components can be manipulated to calculate the effect of increasing patient volume or restructuring patient flow processes (e.g., change the triage process, add an ultrasound machine). Discrete event simulation can facilitate analysis of nonlinear interactions between variables and their intermediary agents; this could be considered "work-as-abstracted."⁷

"Work-as-observed" occurs when care providers know they are being watched, whether informally by trainees or colleagues during patient care, or formally, such as during evaluations (e.g., certification examinations) or as participants in research. The well-known Hawthorne effect posits that participants modify their actions when they know they are being observed.⁸ As a consequence, the work that occurs during, for example, executive walkrounds, may not fully represent the work that occurs in normal situations.

Documentation, fundamentally linked to patient care activities, serves many masters. Documentation is used to communicate meaningful patient care information, support billing, and provide medicolegal information. The accuracy and completeness of "work-as-documented" may be impacted by the skills and memory of the person documenting, the ease or challenge of the documentation process, and the temporal distance between the patient care event and the opportunity to document. When a scribe is added to the documentation process, opportunities for incomplete understanding and miscommunication may arise. Understanding work-as-done by using administrative databases, chart audits, and trigger tools^9-12 relies on work-as-documented.

Claims are written demands for compensation for medical injury, which may be submitted by patients and their families because they have been advised to sue; because they perceive physician dishonesty; because they seek information, resources for future medical costs, or revenge; or for other reasons.^13,14 "Work-as-claimed" is a lagging indicator, often reflecting occurrences that are several years old.^13,15 The relationship between medical malpractice events and medical malpractice claims is complicated and nonlinear.¹⁴ Some claims are without merit, whereas the majority of patients who sustain a medical injury as a result of negligence do not sue.¹³

Simulation uses manikins or other equipment to replicate patient care experiences, allowing healthcare workers to practice their skills without direct risk to patients.¹⁶ Simulations conducted in situ, in actual patient care settings, provide a way to study and improve patient care processes while concurrently enhancing both team and individual patient care skills.¹⁷ A limitless variety of patient care processes can be simulated. Simulations may range from simple tasks such as transporting a patient into a new patient care area or conducting a handoff, to complex tasks such as preparing to implement a new electronic health record module, implementing and maintaining a patient on extracorporeal membrane oxygenation (ECMO), activating a protocol for massive transfusion, or conducting a disaster drill.^18-21 "Work-as-simulated," including skilled debriefing, may come the closest to replicating work-as-done, particularly for uncommon events.²²

Because healthcare delivery is a complex adaptive system, understanding work-as-done is a daunting task, and no single perspective will provide the whole truth. In an extensive review of the advantages and limitations of different methods used to monitor patient safety, Sun asserts, ". . .different methods for detecting patient safety problems overlap very little in the safety problems they detect. These methods complement each other and should be used in combination to provide a comprehensive safety picture of the health care organization."²³ Recognizing the attributes and limitations of each patient safety lens can help facilities develop a more comprehensive and realistic understanding of work-as-done, which can then inform efforts to improve patient safety.

Notes

1. Hollnagel E. Prologue: Why do our expectations of how work should be done never correspond exactly to how work is done? In: Braithwaite J, Wears RL, Hollnagel E, editors. Resilient Health Care. Vol. 3. Reconciling work-as-imagined and work-as-done. Boca Raton (FL): CRC Press, Taylor & Francis Group; 2017. p. xvii-xxv.
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4. Deutsch ES. More than complicated, healthcare delivery is complex, adaptive, and evolving. Pa Patient Saf Advis. 2016 Mar;13(1):39-40. http://patientsafety.pa.gov/ADVISORIES/Pages/201603_39.aspx.
5. Gell-Mann M. Complex adaptive systems. In: Cowan G, Pines D, Meltzer D, editors. Complexity: metaphors, models, and reality. Boston (MA): Addison-Wesley; 1994. p. 17-45.
6. Plsek PE, Greenhalgh T. Complexity science: the challenge of complexity in health care. BMJ. 2001 Sep 15;323(7313):625-8. PMID: 11557716. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1121189/.
7. Day TE, Al-Roubaie AR, Goldlust EJ. Decreased length of stay after addition of healthcare provider in emergency department triage: a comparison between computer-simulated and real-world interventions. Emerg Med J. 2013 Feb;30(2):134-8. Also available: http://dx.doi.org/10.1136/emermed-2012-201113. PMID: 22398851.
8. Sedgwick P, Greenwood N. Understanding the Hawthorne effect. BMJ. 2015 Sep 04;351:h4672. PMID: 26341898. http://www.bmj.com/content/351/bmj.h4672.long.
9. Classen DC, Resar R, Griffin F, Federico F, Frankel T, Kimmel N, Whittington JC, Frankel A, Seger A, James BC. 'Global trigger tool' shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff (Millwood). 2011 Apr;30(4):581-9. PMID: 21471476.
10. Mattsson TO, Knudsen JL, Lauritsen J, Brixen K, Herrstedt J. Assessment of the global trigger tool to measure, monitor and evaluate patient safety in cancer patients: reliability concerns are raised. BMJ Qual Saf. 2013 Jul;22(7):571-9. Also available: http://dx.doi.org/10.1136/bmjqs-2012-001219. PMID: 23447657.
11. Murphy DR, Meyer AN, Bhise V, Russo E, Sittig DF, Wei L, Wu L, Singh H. Computerized triggers of big data to detect delays in follow-up of chest imaging results. Chest. 2016 Sep;150(3):613-20. Also available: https://dx.doi.org/10.1016/j.chest.2016.05.001.
12. Westbrook JI, Li L, Lehnbom EC, Baysari MT, Braithwaite J, Burke R, Conn C, Day RO. What are incident reports telling us? A comparative study at two Australian hospitals of medication errors identified at audit, detected by staff and reported to an incident system. Int J Qual Health Care. 2015 Feb;27(1):1-9. Also available: http://dx.doi.org/10.1093/intqhc/mzu098. PMID: 25583702.
13. Studdert DM, Mello MM, Gawande AA, Gandhi TK, Kachalia A, Yoon C, Puopolo AL, Brennan TA. Claims, errors, and compensation payments in medical malpractice litigation. N Engl J Med. 2006 May 11;354(19):2024-33. Also available: http://dx.doi.org/10.1056/NEJMsa054479. PMID: 16687715.
14. Rothstein MA. Currents in contemporary bioethics. Health care reform and medical malpractice claims. J Law Med Ethics. 2010 Winter;38(4):871-4. Also available: http://dx.doi.org/10.1111/j.1748-720X.2010.00540.x. PMID: 21105950.
15. Kreidler M. 2016 Medical malpractice annual report. Olympia (WA): Washington State Office of the Insurance Commissioner; 2016 Sep 1. 86 p. Also available: https://www.insurance.wa.gov/about-oic/reports/commissioner-reports/documents/2016-med-mal-annual-report.pdf  
16. Deutsch ES. Simulation in otolaryngology: smart dummies and more. Otolaryngol Head Neck Surg. 2011 Dec;145(6):899-903. Also available: http://dx.doi.org/10.1177/0194599811424862. PMID: 21965444.
17. Lockman JL, Ambardekar A, Deutsch ES. Chapter 2.2. Optimizing education with in situ simulation. In: Palaganas JC, Maxworthy JC, Epps CA, Mancini ME, editors. Defining excellence in simulation programs. Philadelphia (PA): Wolters Kluwer; 2015. p. 90-8.
18. Geis GL, Pio B, Pendergrass TL, Moyer MR, Patterson MD. Simulation to assess the safety of new healthcare teams and new facilities. Simul Healthc. 2011 Jun;6(3):125-33. Also available: http://dx.doi.org/10.1097/SIH.0b013e31820dff30. PMID: 21383646.
19. Patterson MD, Blike GT, Nadkarni VM. In situ simulation: challenges and results. In: Henriksen K, Battles JB, Keyes MA, Grady ML, editors. Advances in patient safety: new directions and alternative approaches. Vol. 3. Performance and tools. Rockville (MD): Agency for Healthcare Research and Quality (AHRQ); 2008.
20. Patterson MD, Geis GL, LeMaster T, Wears RL. Impact of multidisciplinary simulation-based training on patient safety in a paediatric emergency department. BMJ Qual Saf. 2013 May;22(5):383-93. Also available: http://dx.doi.org/10.1136/bmjqs-2012-000951. PMID: 23258388.
21. Weintraub AY, Deutsch ES, Hales RL, Buchanan NA, Rock WL, Rehman MA. Using high-technology simulators to prepare anesthesia providers before implementation of a new electronic health record module: a technical report. Anesth Analg. 2017 Jun;124(6):1815-9. PMID: 28207594.
22. Patterson M, Deutsch ES, Jacobson L. Chapter 13: Closing the gap between work-as-imagined and work-as-done. In: Braithwaite J, Wears RL, Hollnagel E, editors. Resilient health care. Vol. 3. Reconciling work-as-imagined and work-as-done. Boca Raton (FL): CRC Press, Taylor & Francis Group; 2017. p. 143-52.
23. Sun F. Chapter 36: Monitoring patient safety problems. In: Shekelle PG, Wachter RM, Pronovost PJ, Schoelles K, McDonald KM, Dy SM, Shojania K, Reston J, Berger Z, Johnsen B, Larkin JW, Lucas S, Martinez K, Motala A, Newberry SJ, Noble M, Pfoh E, Ranji SR, Rennke S, Schmidt E, Shanman R, Sullivan N, Sun F, Tipton K, Treadwell JR, Tsou A, Vaiana ME, Weaver SJ, Wilson R, Winters BD. Making health care safer II: an updated critical analysis of the evidence for patient safety practices. Evidence report/technology assessment no. 211, Agency for Healthcare Research and Quality. Evid Rep Technol Assess (Full Rep). 2013 Mar;(211):1-945. Also available: https://www.ncbi.nlm.nih.gov/books/NBK133411/#ch36.s9