From Digital Dawn to Data Entry: How Healthcare Software Began Stealing Time Part 2 of 5

In part one of this series, we followed healthcare’s journey from paper purgatory to the first flickers of a digital dawn: mainframes at the Mayo Clinic, the Regenstrief Institute’s pioneering records, and the paperless dreams of the 1990s. This second instalment picks up where that story left off, and it takes an uncomfortable turn. As software spread through hospitals and clinics, something unexpected happened: the tools built to give doctors more time for patients began to take it away. My work across South Africa, Zambia, Mozambique, and the USA has shown me this paradox up close, from American doctors typing notes late into the night to South African nurses completing the same register twice, once on paper and once on a screen. Let’s trace how the helpers became the thieves.

“The patient in the bed has almost become an icon for the real patient who is in the computer. I’ve actually coined a term for that entity in the computer. I call it the iPatient. The iPatient is getting wonderful care all across America. The real patient often wonders, where is everyone? When are they going to come by and explain things to me? Who’s in charge?”

Abraham Verghese, MD, Professor of Medicine, Stanford University [1]

One System Becomes Many: The Rise of the Specialised Tools

The pioneers we met in part one dreamed of a single digital record. What the market delivered instead was an explosion of specialised tools, each solving one department’s problem brilliantly while ignoring everyone else’s. Medical Information Technology, better known as Meditech, opened its doors in 1969, and 1979 alone gave us both Epic (then called Human Services Computing) and Cerner (then PGI & Associates) [2]. Radiology went digital along its own dedicated route: the University of Kansas installed one of the first picture archiving and communication systems (PACS) in 1982, and the DICOM standard followed in 1993 to tame the chaos of incompatible imaging formats [3]. Laboratories, pharmacies, billing offices, and emergency departments each acquired systems of their own.

Every one of these tools was a rational purchase, and many were excellent at their jobs. Collectively, though, they turned hospitals into digital patchworks. Perhaps the clearest evidence of the problem is that an entire standards organisation, Health Level Seven (HL7), had to be founded in 1987 simply to help these systems exchange data with one another [4]. When your software needs its own diplomatic corps, fragmentation has already won.

The Great Acceleration: Mandates and Meaningful Use

Adoption still needed a push, and in 2009 it arrived. The American HITECH Act poured roughly 36 billion US dollars into electronic health record (EHR) adoption, tying incentive payments to “meaningful use” requirements [5]. On paper, it worked spectacularly: today more than 99% of US hospitals and 91% of office-based doctors run a certified EHR [6]. The paperless dream of the 1990s was finally realised.

But the speed and the incentives shaped the products. Systems were bought to satisfy regulators and billing departments, and the demands of reimbursement seeped into every template and mandatory field. As Downing and colleagues argued in the Annals of Internal Medicine, billing and regulatory requirements, not poor design alone, are the root cause of the documentation burden, which helps explain why the average American clinical note has grown to roughly four times the length of notes in other countries [7].

The Anatomy of Time Theft

Then researchers started the stopwatch, and the numbers were damning. In a time and motion study across four specialties, Sinsky and colleagues found that doctors spent just 27% of their office day in direct clinical face time with patients and 49.2% on EHR and desk work: for every hour with a patient, nearly two more went to the screen, followed by another one to two hours of EHR tasks each night [8]. Wisconsin researchers who logged primary care doctors’ EHR activity measured 5.9 hours of an 11.4 hour workday spent inside the record, including nearly 90 minutes of work outside clinic hours, the portion doctors have come to call “pyjama time” [9]. And in one American emergency department, doctors averaged about 4,000 mouse clicks over a single 10 hour shift, spending 44% of their time on data entry and only 28% in direct patient contact [10].

Nobody planned this. No committee ever voted to move doctors’ attention from the bedside to the keyboard. It happened click by click, field by mandatory field: a quiet, systematic theft of the clinical hour. And the thief was never software alone. Regulation, reimbursement rules, workflow choices and the code that faithfully encoded them all took their share.

When Help Becomes Noise: Alerts, Overrides, and Unintended Errors

Even the safety features developed frictions of their own. As early as 2004, Ash and colleagues documented how patient care information systems generate their own species of error, born of the mismatch between rigid software and the messy reality of clinical work [11]. A year later, Koppel’s team reported in JAMA that a widely used computerised physician order entry (CPOE) system of that era actually facilitated 22 distinct types of medication error risk, from fragmented displays to confusing order screens [12]. Newer systems addressed many of those specific flaws, but the durable lesson stands: software can introduce new failure modes even while it removes old ones. The alerts meant to catch mistakes told a similar story. A systematic review found that between 49% and 96% of drug safety alerts are overridden, and, importantly, that many of those overrides are clinically appropriate [13]. The failure is not that doctors ignore warnings. It is a signal-to-noise ratio so poor that the rare warning that matters arrives dressed identically to the hundreds that do not.

The Human Cost: Burnout by a Thousand Clicks

The strain shows up in the doctors and nurses themselves. In a national study of 6,560 US doctors, Shanafelt and colleagues found burnout in 57.2% of EHR users compared with 44.6% of non-users, and CPOE use was independently associated with roughly 29% higher odds of burnout [14]. Gardner and colleagues found that about 70% of doctors using EHRs reported health IT related stress, and that those with insufficient time for documentation carried nearly triple the odds of burnout symptoms [15]. These are observational studies: they establish association, not proof that the technology causes the burnout. But the associations all point the same way, and they come with a usability verdict attached. When Melnick and colleagues asked doctors to rate their EHRs on the standard System Usability Scale, the mean score of 45.9 earned a grade of F, placing these tools in the bottom decile of technologies ever assessed with the instrument, and every single point of improvement was associated with measurably lower odds of burnout [16]. A major 2019 investigation by Fortune and Kaiser Health News gathered the whole sorry picture under a title that says it all: “Death by a Thousand Clicks” [5]. Verghese’s iPatient, in other words, is thriving. It is the humans on both sides of the screen who are paying.

A View from the South

It would be a mistake to read this as a purely American story. In South Africa’s public clinics, the frictions are starker still. A 2023 study of EHR implementation describes systems that are “more like back entry capture of paper registers”: nurses record care on paper, and clerks then retype the data into computers [17]. The same study reported that nearly 40% of public facilities lacked reliable internet access, and the National Digital Health Strategy openly names fragmented and poorly coordinated systems as a central challenge [17, 18]. Where an American doctor loses time to one system, a South African nurse often feeds two, one paper and one digital, in the most literal form of double documentation. Time theft, it turns out, is a global franchise; it simply adapts to local conditions.

The Hour We Owe the Patient

None of this story has a villain. Every specialised system was bought to solve a real problem, every regulation targeted genuine waste and error, and every alert was designed to prevent harm. Yet the sum of these rational decisions is a workday in which the computer, not the patient, has become the doctor’s most demanding customer. The foundations laid in part one gave us digital records. This chapter shows what they quietly cost us: the clinical hour itself.

In part three, we’ll dig into the interoperability puzzle: why, decades after HL7 first tried to broker peace, patient information still struggles to follow the patient, and what the long war over standards and data silos means for the AI systems now arriving in our clinics.

References

1. Verghese A. A Doctor’s Touch. TEDGlobal, 2011. https://www.ted.com/talks/abraham_verghese_a_doctor_s_touch. See also: Verghese A. Culture Shock: Patient as Icon, Icon as Patient. New England Journal of Medicine. 2008;359(26):2748-2751.

2. Becker’s Hospital Review. New technology, ancient origins: How Epic, Cerner & more got their names. https://www.beckershospitalreview.com/healthcare-information-technology/ehrs/new-technology-ancient-origins-how-epic-cerner-more-got-their-names/

3. RadSource. The History of PACS: Evolution of PACS. https://radsource.us/history-of-pacs/

4. Health Level Seven International. About HL7. https://www.hl7.org/about/

5. Fry E, Schulte F. Death by a Thousand Clicks: Where Electronic Health Records Went Wrong. Fortune and Kaiser Health News, 18 March 2019. https://fortune.com/longform/medical-records/

6. Office of the National Coordinator for Health IT (ASTP/ONC). National Trends in Hospital and Physician Adoption of Electronic Health Records. HealthIT.gov QuickStats. https://www.healthit.gov/data/quickstats/national-trends-hospital-and-physician-adoption-electronic-health-records

7. Downing NL, Bates DW, Longhurst CA. Physician Burnout in the Electronic Health Record Era: Are We Ignoring the Real Cause? Annals of Internal Medicine. 2018;169(1):50-51. doi:10.7326/M18-0139

8. Sinsky C, Colligan L, Li L, et al. Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Annals of Internal Medicine. 2016;165(11):753-760. doi:10.7326/M16-0961

9. Arndt BG, Beasley JW, Watkinson MD, et al. Tethered to the EHR: Primary Care Physician Workload Assessment Using EHR Event Log Data and Time-Motion Observations. Annals of Family Medicine. 2017;15(5):419-426. doi:10.1370/afm.2121

10. Hill RG Jr, Sears LM, Melanson SW. 4000 Clicks: a productivity analysis of electronic medical records in a community hospital ED. American Journal of Emergency Medicine. 2013;31(11):1591-1594. doi:10.1016/j.ajem.2013.06.028. PMID: 24060331.

11. Ash JS, Berg M, Coiera E. Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-related Errors. Journal of the American Medical Informatics Association. 2004;11(2):104-112.

12. Koppel R, Metlay JP, Cohen A, et al. Role of Computerized Physician Order Entry Systems in Facilitating Medication Errors. JAMA. 2005;293(10):1197-1203.

13. van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of Drug Safety Alerts in Computerized Physician Order Entry. Journal of the American Medical Informatics Association. 2006;13(2):138-147.

14. Shanafelt TD, Dyrbye LN, Sinsky C, et al. Relationship Between Clerical Burden and Characteristics of the Electronic Environment With Physician Burnout and Professional Satisfaction. Mayo Clinic Proceedings. 2016;91(7):836-848.

15. Gardner RL, Cooper E, Haskell J, et al. Physician stress and burnout: the impact of health information technology. Journal of the American Medical Informatics Association. 2019;26(2):106-114. doi:10.1093/jamia/ocy145

16. Melnick ER, Dyrbye LN, Sinsky CA, et al. The Association Between Perceived Electronic Health Record Usability and Professional Burnout Among US Physicians. Mayo Clinic Proceedings. 2020;95(3):476-487. doi:10.1016/j.mayocp.2019.09.024

17. Zharima C, Griffiths F, Goudge J. Exploring the barriers and facilitators to implementing electronic health records in a middle-income country: a qualitative study from South Africa. Frontiers in Digital Health. 2023;5:1207602. doi:10.3389/fdgth.2023.1207602

18. National Department of Health, Republic of South Africa. National Digital Health Strategy for South Africa 2019-2024. https://www.health.gov.za/wp-content/uploads/2020/11/national-digital-strategy-for-south-africa-2019-2024-b.pdf