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Vol 21, No. 07, July 2017   |   Issue PDF view/purchase
Death by Medical Errors: How Evidence-based Information Can Save Lives
Interview with Dr Ujjwal Rao, Clinical Specialist at Elsevier

Senior Clinical Specialist, Elsevier, India

Dr. Ujjwal Rao is Senior Clinical Specialist in Integrated Decision Support Solutions, and is based in India. He provides strategic counsel to health providers on designing world-class clinical decision support systems with Elsevier’s comprehensive suite of current and evidence-based information solutions that can improve the quality and efficient delivery of healthcare.

An experienced emergency physician, executive, clinical informaticist and technology evangelist, Dr. Rao has a decade of experience serving in trust and corporate hospitals in various roles ranging from clinical administration, hospital operations to quality & accreditation. In his former positions, Dr. Rao led EHR implementations for large hospital groups and designed bespoke healthcare analytic solutions to raise profitability.

His passion to see transformation through technology led him to volunteer as a quality consultant with the United Nations. He also currently serves as an Assessor on the Panel of the Quality Council of India for the National Healthcare Accreditation Standards body, NABH.

Dr. Rao obtained his degree in Medicine and then specialized in Hospital and Health Systems Management, Medical Law and Ethics before completing his PhD in Quality and Medical Informatics.

1. You wrote a whitepaper “Order Sets: A Poka-Yoke for Clinical Decisions”. Could you briefly explain how the use of Clinical Decision Support Systems (CDSS) can positively impact patient safety?

Clinicians are decision makers at every step along the patient journey. In other sector-related fields, scientists, researchers and technologists are less involved in the decision making process, more often than not, making a choice between multiple options, whereas clinicians frequently make a decision by “cutting off” all other options until only one or two remain.

Have you ever wondered why the process of making clinical decisions is so arduous? First and foremost, this is because of the growing “tsunami” of medical information. In order to simply keep current with the Primary Care literature, a general practitioner would be need to read medical journals and textbooks for 21 hours every single day [1]! Doctors today cannot help but feel overwhelmed by the medical information explosion. This rapid responsibility expansion in new evidence-based information, which routinely fails to find its way into clinical practice for years after discovery, has resulted in healthcare providers at best often failing to provide the highest value care, and at worst, allowing for truly preventable patient injuries and deaths. While computers can prevent simple avoidable mistakes caused by the most mundane processes (such as illegible hand-writing), the greatest threat to patient safety and the elevated cost of unnecessary tests is this knowledge gap.

Clinical Decision Support (CDS) solutions are major weapons in the battle against preventable medical errors. At the heart of the most impactful CDS lies evidence-based medicine (EBM), which when incorporated into powerful CDS solutions, offers the potential to dramatically and consistently improve healthcare safety and quality by providing clinicians access to current, credible, evidence-based information at all points-of-care. Thus, the decision making process for clinicians can avoid knowledge gap-based errors through the strategic use of CDS.

2. Kindly explain why CDSS is “evidence-adaptive” and how can it be done? Does Elsevier order sets prove useful to solve the multi-factorial healthcare dilemma?

When a physician realizes that he or she needs information, CDS reference solutions provide access to current, credible, evidence-based knowledge, which is either integrated into a hospital’s electronic health record (EHR), available over the internet, or presented in print. Thus by their very nature, reference solutions require that the physician is aware of the fact that he or she does not know something. And with medical knowledge doubling every two months [2], physicians frequently do not know what they do not know. Thus, patients are placed at risk because physicians are unaware that new healthcare information and knowledge is available.

Order sets are the best solution to address this dangerous problem. Order sets automatically push current, credible, evidence-based information specific to the patient’s clinical history and current clinical status directly to the physician at the point of care. Thus, order sets address the knowledge gap, including answering critical questions that the physician doesn’t know he or she should be asking.

However, there is a potential challenge with evidence-based order sets given that clinical knowledge is advancing exponentially. When order sets are implemented but inadequately maintained, they drive providers to practice outdated medicine on a widespread basis [3]. Thus, it is critical for order sets to be evidence-adaptive [4], which means the clinical knowledge within order sets continually reflects current EBM from the research literature, including sources of practice expertise. In the near future, evidence-adaptive order sets will be empowered through advancements in machine learning and artificial intelligence. Today, much evidence adaption is performed manually, with professionals (using computer systems) to rapidly review new EBM in order to update order sets. Elsevier Order Sets combine orderable items with clinical decision support guidance that continually reflect current, credible, evidence-based healthcare information, making this a truly evidence-adaptive solution.

One such example is the successful implementation of Order Sets at University Hospital Frankfurt, which bears testimony to the effectiveness of Elsevier’s outcomes-driven approach. The implementation of order sets focused on gastroenterology care, reduced average length of stay and overall physician ordering time, while elevating physician satisfaction scores for computerized ordering [5].

3. In the whitepaper, it is mentioned that healthcare today faces with new dilemmas: a significant burden of preventable medical errors, an explosion in the rate of medical information growth, and the historically slow adoption of new discoveries. Also with the expanding regulatory demands and rapid rise of medical malpractice litigation, will it be possible for healthcare providers to provide high-quality care and at the same time personalized treatment for patients without errors?

With the multifactorial healthcare dilemma, providers must ask themselves, “Is the practice of medicine no longer humanly possible?” The answer lies in the truth that humans can enhance their capabilities radically through technology, especially knowledge-based technologies like CDS. In order to be safe, effective, and efficient, today’s physicians, nurses, pharmacists, therapists, patients, and other healthcare stakeholders must have rapid, real-time, mobile access to current, credible, evidence-based information.

While many have been disappointed that EHRs have not on their own solved the knowledge gap challenge, it is critical to appreciate that technology is only the vehicle through which information and knowledge is delivered, not itself the primary source of that information and knowledge. In the absence of technology (in fact, long prior to the development of computers and the internet), current, credible, evidence-based information allowed the world’s leading healthcare providers to deliver high quality, cost-efficient care.

Today’s technologies represent a great leap forward in accessing high-value care information and guidance at patient care points, but the provision of evidence-based knowledge and guidance itself is the cornerstone in reducing (and eventually eliminating) preventable medical errors and in consistently improving clinical outcomes.

Moreover, CDS solutions such as order sets, make recommendations that are precisely based on the patient’s condition, complaint, and/or procedure enabling providers to select specific treatment options that are based on current, credible, evidence-based information sources.

4. Is there a correlation between the rise of preventable medical errors and medical information? How can we use technology to reduce preventable medical errors and improve patient outcomes?

The frequency of preventable medical errors resulting in patient injury and death is staggering. It is estimated that for every 100 hospitalizations, approximately 14 adverse events occur, translating to roughly 43 million avoidable patient injuries worldwide each year [6]. In terms of quality of life for those inadvertently hurt, the loss is nearly 23 million years of healthy life [6]. In the United States, avoidable medical errors are the third leading cause of death for adults, with estimates ranging between 200,000 and 400,000 lost patients every year [7].

Medical errors broadly occur as a result of two kinds of failures: knowledge-based and systems-based errors. Knowledge-based errors, more often than not, can directly be attributed to the “Knowledge Dilemma,” in which all medical knowledge will soon double every 73 days [2], yet it takes an average of 17 years for only 14% of new scientific discoveries to “diffuse” into daily clinical practice [8]. For example, β-blockers, a class of drugs whose beneficial effect for heart attack patients was established almost 30 years ago, are still widely under-prescribed [9]. This is but one example of an avoidable medication error (a common cause of preventable patient injury and even death). Medication errors are not only knowledge-bases and are amongst 16 major types of causative system failures identified in a system analysis of a large sample of serious mistakes [10]. All of the top eight were deemed preventable through the provision of better medical information. Thus, there is definitely a very strong correlation between preventable medical errors and inadequate availability and/or use of current, credible, evidence-based medical information.

CDS which incorporates evidence-based order sets can reduce up to 81% of medication errors [11], and today, order sets represent the most impactful CDS solution to empower physicians in the consistent delivery of high value healthcare. Evidence-based care is most impactful when current, credible, evidence-based knowledge is routinely incorporated into the provider workflow (customary work routine). As a result, the most advanced CDS solutions are “workflow-integrated.” Intuitive technology has the potential to completely transform the provider workflow, making it effective and efficient, but when the workflow itself is empowered with actionable knowledge based on current, credible, evidence-based information, there is a profound impact on the providers’ ability to deliver the highest quality, most cost-efficient, evidence-based patient care.

5. How can the advancements in technology such as machine learning and artificial intelligence help physicians to stay updated with latest developments and discoveries?

While artificial intelligence (AI) holds great promise in fields like autonomous cars and robotics, it is unlikely that AI will ever completely replace physicians and other providers. Recently there has been a great deal of excitement because an artificial neural network beat the top human players at “Go”, an inherently complex game offering players millions of possible moves [12]. But technologists find it hard to imagine the even greater complexities in clinical decision making, which are really so convoluted as to make algorithmic replication even more difficult.

What is relevant in healthcare today is intelligence augmentation (IA), where technology amplifies the decision-making capabilities of human providers. CDS solutions such as order sets (that push current, credible, evidence-based information specific to the patient’s clinical history and current clinical status directly to the physician at the point of care) can dramatically simplify, amplify, and clarify the process of clinical decision making. Similarly, care plans, clinical practice guidelines, and clinical pathways that are evidence-adaptive and workflow-integrated, provide current, credible information at the point-of-care, augmenting human intelligence and transforming the quality and safety of healthcare. Thus, when it comes to healthcare, IA prevails over AI.

Whitepaper Summary

Order Sets: A Poka-Yoke for Clinical Decisions

The burden of preventable medical errors is a stark reality in today’s global healthcare systems. Providers of all disciplines are faced with an unimaginable explosion in medical discoveries and knowledge that increasingly risks knowledge-based errors. In addition, the complexities of incorporating this new knowledge into ever increasingly complex healthcare delivery systems drives even more system-based preventable medical errors. Can providers avoid these two types of dangerous routes of preventable patient injury and death when making clinical decisions? Can we learn from the Japanese lean manufacturing strategy known as Poka (unintended mistake) Yoke (avoid) – roughly translated as “error proofing?” Elsevier, a global leader in clinical information solutions, dives deeper into this issue and its impact on patient safety in a whitepaper authored by Dr. Ujjwal Rao, Senior Clinical Specialist.

Dr. Rao examines a major dilemma facing healthcare today – an explosion in the rate of medical information growth challenged by the extremely slow adoption of new discoveries into routine clinical practice. Simply stated, more often than not a physician doesn’t know what he or she doesn’t know. Thus, patients are placed at risk of preventable medical errors, as physicians are unaware that new care delivery information and knowledge is available. For every 100 hospitalizations, approximately 14 adverse events occur, translating to roughly 43 million avoidable patient injuries worldwide each year.

In the pursuit of empowering physicians to consistently provide the highest quality, most cost-efficient healthcare, Clinical Decision Support Systems (CDSS) are being hailed as a major weapon in the battle against the knowledge gap and preventable medical errors.

CDSS, which incorporate order sets and other powerful solutions which rapidly deliver current, credible, evidence-based information to providers whenever and wherever needed, can reduce medication errors by up to 81% (and this is just one form of preventable error). Order sets are especially powerful in that these CDS solutions automatically push current, evidence-based information specific to the patient’s clinical history and clinical status directly to the physician at the point-of-care. As such, today, order sets represent the most immediately impactful CDS solution in the drive towards the consistent delivery of safe, high quality, cost-efficient healthcare.


  1. Alper, Brian S., et al. "How much effort is needed to keep up with the literature relevant for primary care?." Journal of the Medical Library Association 92.4 (2004): 429
  2. Densen, Peter. "Challenges and opportunities facing medical education." Transactions of the American Clinical and Climatological Association 122 (2011): 48.
  3. Bobb, Anne M., Thomas H. Payne, and Peter A. Gross. "View point: controversies surrounding use of order sets for clinical decision support in computerised provider order entry." Journal of the American Medical Informatics Association 14.1 (2007): 41-47.
  4. Sim, Ida, et al. "Clinical decision support systems for the practice of evidence-based medicine." Journal of the American Medical Informatics Association 8.6 (2001): 527-534.
  5. Zwack, Laura. Electronic Order Entry with Order Sets at University Hospital Frankfurt.Munich:Elsevier, 2016. Print.
  6. Jha, Ashish K., et al. "The global burden of unsafe medical care: analytic modelling of observational studies." BMJ quality & safety 22.10 (2013): 3809-815.
  7. James, John T. "A new, evidence-based estimate of patient harms associated with hospital care." Journal of patient safety 9.3 (2013): 122-128.
  8. Balas, E. Andrew, and Suzanne A. Boren. "Managing clinical knowledge for health care improvement." Yearbook of medical informatics 2000.2000 (2000): 65-70.
  9. Bradley, Elizabeth H., et al. "Quality improvement efforts and hospital performance: rates of beta-blocker prescription after acute myocardial infarction." Medical care 43.3 (2005): 282-292.
  10. Leape, LucianL., et al. "Systems analysis of adverse drug events." Jama 274.1 (1995): 35-43.
  11. Bates, David W., et al. "The impact of computerised physician order entry on medication error prevention." Journal of the American Medical Informatics Association 6.4 (1999): 313-321.
  12. Zheping Huang. “Google’s AlphaGo AI secretively won more than 50 straight games against the world’s top Go players” QUARTZ, Jan. 2017. Web. https://qz.com/877721/the-ai-master-bested-the-worlds-top-go-players-and-then-revealed-itself-as-googles-alphago-in-disguise/

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