I am still at the INFORMS Healthcare Conference in Rotterdam.  Brian Denton gave a plenary talk entitled “Optimization for medicine: past, present, and future,” where he made the case for applying more optimization to healthcare problems. He started his talk by introducing the number of papers in PubMed that use operations research methodologies to healthcare problems, and he noted that optimization methodologies were the least used (but not the least valuable!)

Denton introduced several optimization problems from the 1960s that applied optimization to healthcare problems, including linear programming for optimization chemotherapy and nonlinear optimization for optimizing dialysis decisions (e.g., when to change the bath). One dared to solve a linear program with 72 variables! (To be fair, that was tough using 1968 computers). It’s always interesting to learn where operations research gained traction early on. I once learned that the The US Forest Service had been using linear programming models to plan long-range harvesting and tree rotations since the 1960s (see my post here).

Denton highlighted some current optimization applications in healthcare, including Timothy Chan’s work in inverse optimization, Sommer Gentry’s work on matching problems for optimizing kidney exchanges, and his own work on Markov decision processes (MDPs) for optimizing treatment policies for type II diabetes. Denton discussed the importance of personalized medicine, since physicians may adopt many different treatment protocols to treat their patients without nearly enough guidance. “No one can ever agree what to do in medicine,” Denton told us and noted that despite so much disagreement among physicians about treatment options, we end up with non-personalized, one-size-fits-all treatment recommendations. He has been using robust MDPs to identify personalized type II diabetes treatment protocols in an adversarial setting, where the adversary chooses the transition probabilities in an uncertainty set subject to a budget.

Denton ended his talk with several opportunities for research, including how to make inter-related sequential decisions over time. Some research has been done in this area, but much of the research assumes that we know what will happen downstream, which is used to inform decisions we need to make now. He also argued that medical devices provides many challenging opportunities for optimal control problems for delivering dosage, such as an artificial pancreas that delivers insulin. And finally, he mentioned that most research considers a single disease and medical specialty. The next challenge is optimizing across multiple medical “silos” and multiple types of medical specialties, with perhaps a fixed “budget.”

The INFORMS Healthcare conference was really great and was filled with high quality talks. It was a nice mixture of theory and application, with plenty of discussion about how healthcare systems work in international settings.

Related post:

• Data analytics for personalized medicine from the INFORMS Healthcare conference

I’m ending my post with a couple of pictures from Rotterdam

I am at the INFORMS Healthcare Conference in Rotterdam. Dimitris Bertsimas at MIT delivered the opening plenary entitled “Personalized Medicine: A Vision for Research and Education.” He talked about research in operations research, healthcare analytics, and opportunities for analytics education. It was a great talk, where Bertsimas discussed how analytical methods could and should be used for making personalized medical decisions. He was frank and honest about some of the mistakes he made along the way, and those confessions were the best parts of the talk. I captured the talk outline in a picture.

Bertsimas claimed that data is often an afterthought in many models. I agree. His main takeaway that generated a lot of questions dealt with model transparency. Bertsimas stressed the need to make models transparent so that they can be adopted by physicians and healthcare service providers. He warned that models will be “dead on arrival” if they are not transparent. However, transparency can be a challenge when using many machine learning methodologies such as neural networks. He confessed he learned that transparency is far more important than accuracy the “hard way.”

Side note: transparency is not just a sticking point with physicians. The New York City Police Department’s Domain Awareness System was a 2016 Edelman finalist. Police officers also demanded model transparency. This limited the kinds of analytics that could be used within the tool, but the 30,000 police officers bought into the transparency, used the tool, and kept New York City safer.

Have you ever been required to sacrifice accuracy for transparency?

For decades, every commercial air traveler was asked two standard questions:

1. “Has your luggage been in your possession at all times?”
2. “Has anyone given you anything or asked you to carry on or check any items for them?”

Eventually, this stopped after billions of passengers kept saying no. I remember the airlines and/or the Transportation Security Administration stopped asking these questions because they required resources (employee time) without adding to security. I couldn’t find much documentation about this process, so if you find some, please leave a comment.

I wish my doctor’s office would adopt this strategy. I recently had to verify my insurance information and identity three times for a simple doctor’s appointment:

1. when making my appointment,
2. upon check-in for my appointment,
3. with the nurse who took my vitals during my visit,

I realize that my identity needs to be verified at each appointment to insure that my healthcare provider is treating the right person. However, most of the effort seems to be redundant checks to ensure that my insurance information is correct to facilitate billing.

The National Academies released a report entitled The Healthcare Imperative: Lowering Costs and Improving Outcomes: Workshop Series Summary. The chapter entitled “Excess Administrative Costs” starts as follows.

Administrative costs in the United States consumed an estimated $156 billion in 2007, with projections to reach $315 billion by 2018 (Collins et al., 2009). With the time, costs, and personnel necessary to process billing and insurance-related (BIR) activities from contracting to payment validation on the provider side and the needs of payers to process claims and credential providers, significant redundancy and inefficiency arises from healthcare administration.

Yep.

The recommendations don’t specifically mention that my service provider should not ask me if my insurance has changed three times or more for each visit, but it’s definitely consistent with the part about “significant redundancy.”

I don’t have the solution. I am just pointing out that the healthcare industry seems to be slower in fixing its inefficiencies than other industries. If you have the solution, let me know.

What are other opportunities for improvement in healthcare operations?

FiveThirtyEight has a feature story on HIV prevention entitled “It Took 20 Years For The Government To Pay For An Obvious Way To Prevent HIV”. It’s a nice article, and I encourage you to read it. Needle exchanges is a simple evidence-based intervention that has drastically reduced the spread of HIV and other diseases among intravenous drug users. It’s not so obvious from reading the article that exchanging needles isn’t really a medical intervention — it’s the simple, low-cost process of letting intravenous drug users drop off dirty needles and pick up some clean needles.

I’m writing about this because Ed Kaplan of Yale (and current President of INFORMS) pioneered this work. He developed a probability  model of HIV transmission and ran the initial tests by labeling needles, lending them to users, and testing the needles for HIV when they were returned. He demonstrated that needle exchanges reduce the transmission of HIV by more than 33%. And then they became a thing. There were no great ways to treat HIV back in 1991–at least by today’s standards–and even now HIV treatment is really expensive. Exchanging needles is really cheap, so it makes more sense to prevent HIV than treat HIV.

I recommend reading Ed’s seminal paper in Statistics in Medicine and the associated Interfaces article. The New York Times had a nice write up of his work back in 1991. It’s hard to overstate the importance of Ed’s work in this area. Many of Ed’s papers are cited and discussed in the 1995 Institute of Medicine report “Preventing HIV Transmission: the role of clean needles and bleach” that supported needle exchanges after a mounting body of evidence suggested that needle exchanges make a difference. I also like Ed’s 1995 article in Operations Research about probability models associated with needle exchanges. The research in this area was put into practice, and the result was lower HIV incidence and lives saved. This is a great example of how operations research can make the world a better place. Ed Kaplan may be a professor but he doesn’t live in an ivory tower.

Read more about Ed Kaplan’s research in Yale Insights.

Ed Kaplan and collaborators on the needle exchange project

Five percent of adults seeking healthcare (12 million adults) have an incorrect or delayed medical diagnosis. These mistakes are costly. They account for 6 to 17 percent of adverse events at hospitals and result in death more than other types of mistakes. Most people will experience at least one of these diagnostic errors in their lifetime, sometimes with fatal consequences.

The Institute of Medicine, of the National Academy of Sciences, issued a report entitled Improving Diagnosis in Health Care that addresses diagnostic errors. The report contains many suggestions for how diagnostic healthcare errors can be reduced. Check it out. The Washington Post also has a nice article on this report.

A previous Institute of Medicine 2000 report on patient safety (“To Err is Human“) addressed other types of safety issues that involve human factors issues after a diagnosis has been made. This was a landmark report that led to many safety and quality improvements in healthcare (and great research at the ISYE department at UW-Madison!). However, diagnostic errors received little attention since the publication of this report despite being a problem.

The committee for the Improving Diagnosis in Health Care report is largely composed of medical personnel with at least one notable exception: my UW-Madison ISYE colleague Pascale Carayon at UW-Madison. To a large extent, diagnosis is a medical problem: there are thousands of conditions, many of which are rare, and it’s hard to match the correct single diagnosis with a set of ambiguous outcomes and test results. I appreciate how hard this problem is, and I’m impressed that so many doctors get it right the first time. Medical expertise is a necessary first ingredient.

But medical diagnosis is also a systems problem. Earlier I blogged about the report “Operations Research – A Catalyst for Engineering Grand Challenges” that summarized ways OR can address engineering grand challenges from the National Academy of Engineering. One of the four challenge areas in this report was “OR for human health,” and treatment and diagnostic issues fell under this area. Diagnosis is increasingly a systems issue, since diagnosis is often a function of medical tests and medical imaging. OR is good at weighing the costs and benefits of diagnosis and treatment since Type II errors are often really costly.

I’ve just made a plug for OR and medical diagnosis, but to be honest, I mainly read articles for planning treatment once a positive diagnosis has been made. One important paper in the literature develops linear programming-based machine learning techniques to improve breast cancer diagnosis (more UW-Madison research!):

Mangasarian, O. L., Street, W. N., & Wolberg, W. H. (1995). Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), 570-577.

Let me know of any OR work in the area of medical diagnosis in the comments. Kudos to the committee who produced the  Improving Diagnosis in Health Care report – I hope it leads to new important research in OR and industrial engineering.

Related posts:

• Engineering grand challenges that operations research can help solve
• Type II errors are the ones that get you fired
• Healthcare in the age of analytics

INFORMS has a new volume of its Editor’s Cut that is a collection of resources for healthcare in the age of analytics [Link]. Healthcare is starting to adopt advanced analytical methods to improve health and healthcare delivery, and this volume is a starting point for learning more about analytics for healthcare. Volume resources include research articles, trade journal articles, videos, and podcasts. Here is the introductory video starring volume editor M. Eric Johnson of Vanderbilt University.

Here is the list of recent research articles about healthcare analytics: Sadly, the articles are paywalled, but you can access the articles if your institution has a subscription.

The Vital Role of Operations Analysis in Improving Healthcare Delivery
Linda V. Green

Predictive Analytics for Readmission of Patients with Congestive Heart Failure
Indranil Bardhan, Jeong-ha (Cath) Oh, Zhiqiang (Eric) Zheng, Kirk Kirksey

Feeling Blue? Go Online: An Empirical Study of Social Support Among Patients
Lu Yan, Yong Tan

Electronic Medical Records and Physician Productivity: Evidence from Panel Data Analysis
Hemant K. Bhargava, Abhay Nath Mishra

Business Analytics Assists Transitioning Traditional Medicine to Telemedicine at Virtual Radiologic
Ersin Körpeoğlu, Zachary Kurtz, Fatma Kılınç-Karzan, Sunder Kekre, Pat A. Basu

Offering Pharmaceutical Samples: The Role of Physician Learning and Patient Payment Ability
Ram Bala, Pradeep Bhardwaj, Yuxin Chen

The Weighted Set Covering Game: A Vaccine Pricing Model for Pediatric Immunization
Matthew J. Robbins, Sheldon H. Jacobson, Uday V. Shanbhag, Banafsheh Behzad

Multiregional Dynamic Vaccine Allocation During an Influenza Epidemic
Anna Teytelman, Richard C. Larson

The Digitization of Healthcare: Boundary Risks, Emotion, and Consumer Willingness to Disclose Personal Health Information
Catherine L. Anderson, Ritu Agarwal

Process Management Impact on Clinical and Experiential Quality: Managing Tensions Between Safe and Patient-Centered Healthcare
Aravind Chandrasekaran, Claire Senot, Kenneth K. Boyer

Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department
Robert J. Batt, Christian Terwiesch

Commentaries to “The Vital Role of Operations Analysis in Improving Healthcare Delivery”

Multilevel Simulations of Health Delivery Systems: A Prospective Tool for Policy, Strategy, Planning, and Management
Hyunwoo Park, Trustin Clear, William B. Rouse, Rahul C. Basole, Mark L. Braunstein, Kenneth L. Brigham, Lynn Cunningham

Value-in-Context of Healthcare: What Human Factors Differentiate Quality of Nursing Services?
Hironobu Matsushita, Kyoichi Kijima

Information Hang-overs in Healthcare Service Systems
Atanu Lahiri, Abraham Seidmann

Active Social Media Management: The Case of Health Care
Amalia R. Miller, Catherine Tucker

Privacy Protection and Technology Diffusion: The Case of Electronic Medical Records
Amalia R. Miller, Catherine Tucker

Related posts:

• engineering grand challenges that operations research can help solve
• healthcare is an engineering problem

A new report by the  President’s Council of Advisors on Science and Technology (PCAST) is all about how health care needs systems engineering solutions [Press release here]. The report entitled Better Health Care and Lower Costs: Accelerating Improvement through Systems Engineering outlines the various ways in which industrial and systems engineering can help. Several OR methods and tools are listed in the report, including operations management, queuing theory, simulation, supply-chain management.

Rising healthcare costs are the motivation for this report. The United States spends more (much more!) for healthcare than any other country.

Healthcare costs by country, courtesy of the WSJ. “In 2011, the most recent year in which most of the countries reported data, the U.S. spent 17.7% of its GDP on health care, whereas none of the other countries tracked by the OECD reported more than 11.9%. And there’s a debate about just how well the American health-care system works. As the Journal reported recently, Americans are living longer but not necessarily healthier .”

Healthcare costs are expensive and rising in every country, but they are rising in the US much faster than any other country on the planet. It is unsustainable. If we forecast healthcare costs to our children and grandchildren, we can easily imagine a future where we spend so much on healthcare that we cannot sustain other important programs that benefit society (like education!).

Growth in healthcare costs is higher in the US than in other countries.

The report addresses the healthcare cost problem:

This report comes at a critical time for the United States. Health-care costs now approach a fifth of the U.S. economy, yet a significant portion of those costs is reportedly “unnecessary” and does not lead to better health or quality of care. Millions more Americans now have health insurance and therefore access to the health care system as a result of the Affordable Care Act (ACA). With expanded access placing greater demands on the health-care system, strategic measures must be taken not only to increase efficiency, but also to improve the quality and affordability of care.

Other industries have used a range of systems-engineering approaches to reduce waste and increase reliability, and health care could benefit from adopting some of these approaches. As in those other industries, systems engineering has often produced dramatically positive results in the small number of health-care organizations that have implemented such concepts. These efforts have transformed health care at a small scale, such as improving the efficiency of a hospital pharmacy, and at much larger scales, such as coordinating operations across an entire hospital system or across a community. Systems tools and methods, moreover, can be used to ensure that care is reliably safe, to eliminate inefficient processes that do not improve care quality or people’s health, and to ensure that health care is centered on patients and their families. Notwithstanding the instances in which these methods and techniques have been applied successfully, they remain underutilized throughout the broader system.

It makes 7 main systems engineering recommendations:

• Recommendation 1: Accelerate the alignment of payment incentives and reported information with better outcomes for individuals and populations.
• Recommendation 2: Accelerate efforts to develop the Nation’s health-data infrastructure.
• Recommendation 3: Provide national leadership in systems engineering by increasing the supply of data available to benchmark performance, understand a community’s health, and examine broader regional or national trends.
• Recommendation 4: Increase technical assistance (for a defined period—3-5 years) to health-care professionals and communities in applying systems approaches.
• Recommendation 5: Support efforts to engage communities in systematic healthcare improvement.
• Recommendation 6: Establish awards, challenges, and prizes to promote the use of systems methods and tools in health care.
• Recommendation 7: Build competencies and workforce for redesigning health care.

I have two new posts on the INFORMS Healthcare Conference blog. The first argues that optimization is a helpful tool for healthcare ORMS (Analytics isn’t just statistics). The second explores the role that patient outcomes–the gold standard in top medical journal papers–should play in operations research models for health.

My second post for the INFORMS Healthcare Conference blog is available. It is about unstructured data and a future with healthcare providing robots [Link].