Healthcare 2020

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Vaccines.  Trauma Centers. Emergency Departments. Hospital Readmissions. Up-Coding. Over-testing. Medical Reporting. Physician Ownership. Pharma. Insurance. Government. Cancer. Organ Transplants. Kidney Exchange. Opioids. Disease Prevention. Screening. Detection. Global Health Funding Allocation. Primary Care. Ambulatory Care. Inpatient Care. Residential Care. Long Term Care. Concierge Medicine. Personalized Medicine. Telemedicine. Precision Medicine.

Healthcare Operations (47-762) is a PhD Course that I teach every other year, in the Fall, alternating with Foundations of Operations Management, which I previously discussed in my post Inventory Models in Service of Practice.

What are the technical skills that I would like PhD students to have so that they can tackle the recognizably healthcare topics listed above in a systematic and sophisticated manner? 

The three traditionally Operations Research tools are:

  Markov Decision Processes

  Queuing Theory

  Non-Linear Mixed-Integer Linear Programming

It is indeed good to schedule appointments well and make sure the operating rooms are being utilized efficiently, as it is to ensure that the patients are not waiting too long to obtain their service and so on.

This is HOM v1.0.

Let me be somewhat provocative here:

          Newtonian Physics: Special Relativity == HOM v1.0: Game Theory

Yes, traditional OR folks can be sometimes clueless as to the healthcare eco-system in which they are optimizing and/or improving processes and performance.

I am not even invoking General Relativity or Quantum Field Theory here!

Beyond local optimization, it is additionally important to question:

Why a patient is being sent to be tested in the first place (“was this visit even necessary?”).

Are being released from the hospital way too early (“are you sure the patient has fully recovered?”).

It is important to question which nurses are being scheduled in long-term care:

Are they full-time employees or agency temps?

It is important to question how the decisions on what vaccine to make, and when to start making it, and what happens (and to who) if there are too few (or too many) in a given season. 

It is important to know which patients are even listed for transplants, and which organs are allocated or discarded (while patients die on the wait list).

Is that fair and equitable?

That is:

it is important to also know why the operations are facing the inputs and the constraints that they are.

It is important to know what is being measured, and why, and how that relates to who is getting reimbursed (and for what amount) and/or penalized.

So, to the three traditionally OR topics, I have added:

Game Theory

Queuing Games

What is missing still?

 Data Science.

This brings me to the final two topics in this course that make up the technical capabilities for HOM v2.0:


Machine Learning

 It has always been my mantra that:

The way things are does not mean that it is the way they should be.

This is not your father’s Healthcare Operations PhD Course. (And, yes, we will discuss operational strategies in times of COVID19).  😉

1 comment

  1. Is there a way for non program students to take this course? I have been thinking about these topics for the past several years and never found things organized so well. Agree wholeheartedly with your assessment that the way things are does not mean that is how they should be. Especially in healthcare.

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