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National Academy of Engineering (US) and Institute of Medicine (US) Committee on Engineering and the Health Care System; Reid PP, Compton WD, Grossman JH, et al., editors. Building a Better Delivery System: A New Engineering/Health Care Partnership. Washington (DC): National Academies Press (US); 2005.

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Building a Better Delivery System: A New Engineering/Health Care Partnership.

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Patient Trajectory Risk Management

Charles Denham

HCC Corporation and

Texas Medical Institute of Technology

This paper addresses the notion of risk trajectory of individual patients and the resultant aggregate risk trajectory of the healthcare enterprise caring for populations of patients. It also describes the use of various engineering concepts applied to medicine.

In the late 90's, working with a team from the Institute for Healthcare Improvement (IHI) and Premier Inc. a group purchasing organization of 1,800 hospitals we focused our attention on medication management. The project involved collaborators from the Cleveland Clinic, Partners System, Harvard Medical School, Mayo Health System, a number of frontline hospitals and leading experts. Our goal was to identify the idealized design for medication management to reduce adverse drug events, a major cause of preventable death and disability in U.S. hospitals. To do that, we first had to identify achievable world-class performance, then the “is state” of frontline hospital performance, and finally processes and technologies that would enable us to close the gap between the two. We were surprised by our findings and gratified by the opportunities they revealed.

Engineers are used to using process impact evaluations, risk analyses, and pattern recognition methods, however these are new to the practice of medicine at frontline institutions. Clearly, medicine has much to gain from engineering, and many benefits have yet to be realized.

The Institute of Medicine report, Crossing the Quality Chasm (IOM, 2001), proposes that we must redesign healthcare so that it is patient centered, evidence based, and systems focused. As such we must have a much better understanding of “integrated performance”—i.e. operational, clinical, and financial processes and outcomes—of an individual patient's care delivery through a healthcare episode. We must look at the performance/risk trajectories of common patient treatment process paths and examine the contributive impact to enterprise wide performance. Hospital administrators must step back from their traditional vertical business unit view and take into account their patient populations as they move through those vertical units so that they can recognize operational innovations that can eliminate process segment failures.

The game of golf provides a powerful metaphor. The desired outcome is to deliver the ball to the hole. For a given link one golfer may take eight strokes and another might take three. Both reach the goal if the outcome measure was just “ball in hole,” however one expended more energy and time than the other. The golfer taking eight strokes has increased the risk of having mishap along the way. In a similar way, if a patient requires two or three extra days of care, the risk of having an adverse event is greatly increased due to greater exposure to the inherently dangerous hospital environment.

To come up with an ideal design for medication management, we first mapped the clinical and operational processes involved in medication use. Next, we considered the products, services, and technologies involved that enable best or better practice (technologies might include process reengineering tools, for example). Then we identified their impact on the risk of adverse events and whether they closed the gap between typical performance and best achievable performance.

Traditionally administrators and clinicians have been trained to define a medication error by violation of one or more of the “five rights”—the right patient, the right drug, the right time, the right dose, the right route. Such errors occur with virtually every patient admitted to hospital. Dr. David Classen a noted patient safety expert on our team demonstrated that the overlap between error and harm minimal using this definition of error—only a small fraction of harm is caused by error as defined by the “five rights.” A great number of errors do not cause harm, and more importantly a number of adverse drug events that cause death, disability, or require treatment would not normally be counted using the classical “5 rights” framework.

During the idealized design process, we worked with a number innovative healthcare technology suppliers; 70 to 80 percent of them were attuned to error. Few focused on harm. The deeper we explored adverse drug events it became more and more apparent that distinguishing between error and harm was critical. We focused on the most common causes of adverse drug events including transition zones between care teams and high impact intravenous infusion events. We did not ignore errors without harm, but we did not focus on them. After completing about 80 percent of a thorough, evidence-based review of integrated care and operational processes, with the guidance of a number of experts, the opportunities for mitigation started to become clear.

Subsequently IHI led a number of very successful hospital collaborative initiatives using a “trigger tool” medical record review framework that helped identify adverse drug event (ADE) risk and performance gaps.

We studied smart the Alaris smart infusion pumps that have now have the ability of capturing and even preventing the most serious IV adverse events, clearly a technology advance that will deliver dramatic speed to impact in reduction of ADEs.

To illustrate the error-harm gap and the notions of patient trajectory and hospital risk trajectory we used the example case of anticoagulation management with our teams and collaborative groups. Anticoagulant drugs are often very poorly managed by clinicians and patients resulting in severe adverse drug events. In fact this is the area of the most common drug related malpractice claims and awards.

Certain engineering concepts have great application to medicine. When engineers evaluate airplanes, they examine and discuss its performance envelope. We applied this concept to the management of anticoagulation. Warfarin is an anticoagulant drug used to manage patients. Its danger lies in the fact that the therapeutic envelope of safety relating dose to effectiveness and complications may change or shift. The patient's diet (i.e., wine or vitamin K consumption), or liver function can shift the therapeutic window. The therapeutic envelope is always changing, posing huge risk to patients for overdose or under dose leading to clotting or bleeding disorders. Currently physicians try to manage patients undergoing anticoagulation by trying to interpolate and extrapolate the relative patterns of multiple lab values and historical factors. Application of the performance envelope delivers terrific pattern recognition opportunities.

We also demonstrated the use of other aviation tools to communicate performance. For instance we created a mock up “digital dashboard,” illustrating how clinicians could recognize patterns, access relevant protocols, and in the case anticoagulation decide how to manage the patient.

In collaboration with one of the nations leading anticoagulation experts we presented an example case study of a young adult admitted for treatment of a defective heart valve who experienced 11 typical and different adverse drug events, none of which was caused by a medical error (using the 5 right classification) and none of which would have been picked up by the typical methods we use to catch medical errors. Dose adjustments unique to the patient's condition and omissions due to missed laboratory values would not typically be classified as a medication error. The patient eventually has a stroke. In this case, the potential for recognition of the risk for adverse events would have been picked up by a computerized physician order entry (CPOE), which integrates order entries with laboratory and historical information. We know from other studies that CPOE can reduce adverse events dramatically.

In the future, we will have a decision-support systems that enable clinicians who are not specialists in anticoagulation to put that part of the treatment in the hands of a pharmacy team while being able to monitor potential adverse events. That is precisely what an information integrating device that pilots use called a flight director does. Flight information is provided as an input, the crew makes sure all the instrumentation is synchronized and the director follows the plan. If the workload becomes too heavy, the autopilot can be turned on.

Today, 16 different types of specialists prescribe anti-coagulants; none are specialists in anticoagulation. Orthopedists, internists, and cardiologists are all administering the drug and are responsible. The risk trajectories such patients are not being managed well and adverse events such as preventable strokes and bleeding related complications are occurring in epidemic proportions.

We used a mockup of the digital dashboard to study the young adult described earlier. His medical history and his recent history revealed a number of health problems that pre-disposed him to a bleeding and clotting disorder that made anticoagulation drugs extremely dangerous for him. When we asked what might have been done differently, we found that when the care data is reconstituted in a graphic it would allow us to recognize a pattern. Had the data presentation been like that presented in aircraft instrumentation we would have seen the window of safety narrowing and prevented catastrophe. Instead, we are caught by surprise driving from a view through the rear view mirror.

Clinicians could be assisted by innovations that make patterns simpler to recognize. The average doctor in an intensive care unit can interpolate three or four trends. A patient on a respirator who is very ill might have could have 60 pertinent trends. Our slowest cognitive capability is in processing data, which is exactly what computers do well.

Before retiring to focus full time on emerging technologies, I was a radiation oncologist with a very large practice, and I managed all of my patients all the way through therapy. I had a high volume of patients with common diseases, including colon, breast, lung, and prostate cancer. I had to navigate between the response of the tumor to radiation therapy and the response of normal tissue. I had to manage that patient through a safety window that would become narrower and narrower as we proceeded through care. As the dose was increased, the risk for a host of complications would increase and continue intensify through out treatment. We knew that every treatment decision had a risk-benefit balance to it. Every patient had a unique trajectory based on historical data and how certain factors had impact as therapy progressed. These patients were managed based on tacit knowledge—we could tell when a patient was headed for trouble, we could link this to certain parameters.

In working with healthcare technology suppliers, we have found that an evidence-based, patient centered, and systems performance targeted approach to “enabling” best or better practice allows innovations to be developed that improve clinical performance and reduce risk. In addition, they often deliver improved enterprise wide performance as a by-product of improved patient specific performance.

If we had continuity of information with pattern recognition support we could examine the risk trajectory of patients with very complex disorders and create scenarios and real time forecasts, as we do in aviation. In the future, we might ask a medical student to use a computer model to run scenarios for a specific patient. We could graphically portray patterns and risk trajectories to assist in decision making before patients get into trouble. Is the patient's cardiac function adequate? Will his kidneys clear everything? What-if scenarios can be run before events cascade.

Engineers already provide wonderful computational support and pattern recognition solutions for many industries. These technologies will offer physicians a terrific opportunity to “think through” treatment scenarios. With an appropriate decision-support system, we could apply the lessons learned in other industries, such as aviation and aerospace, to complex medical problems. The principles of data analysis from engineering could be tremendously beneficial for health care.


  1. IOM (Institute of Medicine) Washington, D.C: National Academy Press; 2001. Crossing the Quality Chasm: A New Health System for the 21st Century.
Copyright © 2005, National Academy of Sciences.
Bookshelf ID: NBK22869


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