Order Sets: A POKA-YOKE for Clinical Decisions by Dr. Ujjwal Rao, @DrUjjwalRao – Part 2/2

The full potential of a CDSS can be realised when it is seamlessly integrated into the clinical workflow and is evidence-adaptive

In continuation to the the part one of the article, in the part 2 of the “Order Sets: A POKA-YOKE for clinical decisions” Dr. Rao is



A “Physician Order” is a communication directing a particular service or action to be taken in the care of a specific patient. Medications, diet, physical activities, laboratory tests, radiologic studies, therapies, treatments…all are among the literally dozens of orders written to guide the care of each and every patient by the physician throughout an ordinary day. 

Thus the physician ordering process is complex and time-consuming. In addition, the continuous explosion of new evidence-based information results in the reality that providers often make mistakes, at best failing to provide the highest value care, and at worst causing preventable injuries and deaths. And while computers can address avoidable mistakes from the most mundane sources (such as illegible hand-writing), the greatest threat to patient safety and cost waste is the knowledge gap.

Fortunately, when a physician realises that he or she needs information, CDSS reference solutions provide access to current, credible, evidence-based knowledge (either integrated into an EHR, available over the internet, or in print). Thus by their very nature, reference solutions require that the physician knows he or she doesn’t know something.

But medical knowledge is doubling every two months. Clearly many times the physician doesn’t know what he or she doesn’t know… Thus patients are placed at risk because physicians are unaware that new information and knowledge is available.

Order sets are the best solution to 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. Take for example:

A 52 year old man is admitted for surgical treatment of a right-sided colon cancer. His surgeon regularly operates on such patients, removing that segment of large intestine harboring the malignant tumor. But like many, this surgeon is unaware that this patient’s young age and tumor location suggest an inherited syndrome requiring a much more extensive operation to prevent a second cancer over the next decade. 

If the surgeon “doesn’t know what he doesn’t know,” how can he look up “inherited colon cancer” in his CDSS reference solution? He can’t. But when the patient is admitted to the hospital, order sets specific for colon cancer patients are automatically pushed to the physician. These order sets can be commercially available or can be created by the hospital, healthcare system, regional, or international experts (physicians, nurses, pharmacists, etc.) and represent the evidence-based guidelines and information on colon cancer. Thus the order sets educate the surgeon and recommend that he order a simple blood test to check for the inherited cancer syndrome. If integrated within an EHR, the physician can actually click on embedded hyperlinks to view the EBM sources of the recommended orders. 

The surgeon will likely accept the recommended order and confirm that the patient suffers from the syndrome. Then the surgeon can search the CDSS reference solution and rapidly learn the appropriate surgical procedure for the patient, as well as how to test and screen family members for the inherited syndrome.

Thus order sets address the knowledge gap, including providing the physician with what he “doesn’t know he doesn’t know.”

But there is a risk with evidence-based order sets because clinical knowledge is advancing exponentially. When order sets are implemented but inadequately maintained, they drive providers to practice outdated medicine on a widespread basis [14]. Thus it is critical for evidence-based order sets to include a knowledge-base that continually reflects current evidence. 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 for updating order sets. CDSS which incorporate order sets can reduce medication errors up to 81% [15], and today, order sets represent the most impactful CDSS solution to empower physicians in delivering the highest quality, most cost-efficient evidence-based patient care.


One of the greatest challenges of healthcare reform worldwide is the reluctance of those paying for technology to invest in EBM and CDSS. The question, of course, is return on investment (ROI). However, the potential ROI of order sets through reduction in adverse drug events (ADE) and unnecessary diagnostic tests alone is projected to be enormous (in one academic hospital estimated at up to $10 million [16]). Although there remains a dearth of high-quality evidence on the cost impact of order sets, many operational benefits which intuitively link to cost reduction have been demonstrated. Including: reductions in overall length of stay; postoperative length of stay; and the total cost for multiple surgical procedures, including total knee arthroplasty, appendectomy, total laryngectomy, cholecystectomy, carotid endarterectomy, gastrectomy, inguinal hernia repair, and colon surgery [17].

University of Kentucky Healthcare (UKHC) adopted a well-known commercial order sets solution in 2013 [18], demonstrating improvements in compliance to standard practices and elimination of unnecessary tests. At the University Hospital Frankfurt in Germany, implementation of order sets focused on gastroenterologic care reduced average length of stay and overall physician ordering time while elevating physician satisfaction scores for computerised ordering [19].


The multi-factorial healthcare dilemma including preventable medical errors, the information explosion, slow knowledge diffusion, a growing regulatory environment, and increasing litigation has rendered Clinical Decision Support Systems indispensable. 

Order sets are designed not only to answer questions that the physician is asking, but also to answer critical questions that the physician doesn’t know he or she should be asking. Founded in current, credible, evidence-based information, order sets are the most impactful of physician CDSS solutions. 

Combined with reference and other CDSS solutions, order sets have the potential to empower physicians in providing the safest, highest quality, most cost-efficient healthcare; that is, a truly reliable Poka-Yoke.

Suggested Reading

Dr. Ujjwal was also asked in a recent interview with BioSpectrum India, to share more about the challenges, and most urgent needs in today’s healthcare systems. 

Some might argue that technology is the way forward but Dr. Ujjwal is of the view that technology is only the vehicle through which information and knowledge is delivered. High-quality and consistent care needs to be driven by both tech and evidence-based medicine. The full article can be read online here: 


[13]: Sackett, David L., et al. “Evidence based medicine: what it is and what it isn’t.” Bmj 312.7023 (1996): 71-72.

[14]: 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.

[15]: 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.

[16]: Glaser, J., J. M. Teich, and G. Kuperman. “Impact of information events on
medical care. “Proceedings and abstracts of the 1996 Healthcare Information
and Management Systems Society Annual Conference. 1996.

[17]: Ballard, David J., et al. “The Impact of Standardised Order Sets on Quality and Financial Outcomes.” Advances in Patient Safety: New Directions and
Alternative Approaches (Vol. 2: Culture and Redesign). Rockville (MD): Agency
for Healthcare Research and Quality (US); 2008

[18]: Elsevier Clinical Solutions. How Elsevier Helped University of Kentucky Health-Care® Bring Order to Their Order Sets. N.p.: Elsevier Clinical Solutions, 2016. Print.

[19]: Zwack, Laura. Electronic Order Entry with Order Sets at University Hospital Frankfurt.Munich:Elsevier, 2016. Print.

Dr. Ujjwal Rao

Dr. Ujjwal Rao is Senior Clinical Specialist in Integrated Decision Support Solutions, and is based in New Delhi, 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.

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