In this course, you’ll learn the foundational economic theories behind health care innovation and how to optimize your own health care practice or organization. Designed to help you gain a practical understanding of the theoretical frameworks of behavioral economics and operations management in the health care setting, this course will help you apply these frameworks to assess health care practices and apply innovation while managing risk. You’ll also explore the best practices for evaluating one’s innovative practices, using real-life examples of success to see the concepts in action. By the end of this course, you’ll have honed your skills in optimizing health care operations, and be able to develop the right set of evaluations and questions to achieve best innovative practices within your organization.
À partir de la leçon
Module 1
This module was designed to give you a foundational knowledge of health care operations and methods of optimization. You’ll examine the provider-patient relationship and learn how a provider can improve performance measures for the patient by analyzing the different types of performance metrics such as patient-centric, operations, and financial measures. Through discussing the different types of inefficiencies such as waste, variability, and inflexibility, you’ll be able to optimize performance and service. You’ll also learn how to gauge patient flow by flow rate, flow time, and inventory, and be able to map processes using a flow diagram and a Gantt chart. By the end of this module, you’ll be better able to calculate the financial viability of a health care provider using Key Performance Indicators (KPI) and create a framework to assess and optimize performance.
Andrew M. Heller Professor at the Wharton School, Senior Fellow Leonard Davis Institute for Health Economics Co-Director, Mack Institute of Innovation Management The Wharton School
Amol S. Navathe, MD, PhD
Assistant Professor of Medical Ethics and Health Policy Department of Medical Ethics and Health Policy
David A. Asch, MD, MBA
Professor of Medicine and Professor of Medical Ethics and Health Policy Department of Medicine
Roy Rosin, MBA
Chief Innovation Officer Penn Medicine
Kevin Volpp, MD, PhD
Professor of Medicine, Division of Health Policy / Professor of Health Care Management Perelman School of Medicine / The Wharton School
Each clinical setting has its unique challenges and things
can get rather messy when you deal with entire hospitals,
not to mention entire healthcare systems,
but I think it's important to keep a couple of general principles in mind.
The next three sessions each will carry
one important lesson about the interface between operations and finance.
The clinical setting I've picked for this module is a dental office.
Two reasons for that. First, we all have been at the dentist.
I told you about my bad bike accident in
the first module and to be honest most of what you see here is fake.
So believe me, I have spent my hours in dental office.
Second, just as I started working on this module an executive of a maker
of dental equipment contacted me to discuss the topic of operations and finance.
I've no conflict of interest here,
I did not get a penny from this organization or any organization in this space,
but we agreed on a simple deal.
I would give them some advice and in return I would
get some data about dental operations.
We will be using this data as a case study for
the coming sessions to talk about the financial aspects of running a clinical operation.
And given that it's an operations course after all,
we want to think about how these financials interface with the operations.
So let's look at what we would call a good old dental practice.
Our dental practice has one doctor who's owns a practice,
two hygienists and four rooms.
Assuming that we get our dentist share his financial data with us,
chances are that we get something like this.
The practice takes in a significant amount of money as revenue.
The revenue comes from the dentist and the two hygienists.
On the expense side,
consumables include various disposables that are used during the visits.
There are also expenses for lab work,
most of it has to do with counts.
Most of the costs however are in salaries and benefits.
We also have to pay the sales and
general administrative expenses such as any advertising we do,
fees for processing credit cards and so on and finally,
we have to pay for all facilities and keep them clean and neat.
Data like this is summarized in
the profit and loss statement of a business or P&L for short.
What I find most remarkable about such P&L statements,
is that they tell us very little what is actually going on in the practice.
Does it take an hour or two to put in a crown?
Is it easy to get an appointment in this practice?
Is the staff overworked or are we sitting on idle capacity?
Answers to questions like this require very different data.
So, imagine we get friendly with a dentist,
and the dentist shares with us some more operational data. Here's what we learn.
The dental office serves an active panel of 1,600 patients.
Patients on that panel needs some consultations.
They see their hygienist twice a year on average,
and these times at at least a dentist also does some quick checkup on the patients.
Patients need significant procedures like a crown every so often.
As far as operations and finance are concerned,
the dentist takes a given amount of time per consult which he or she gets reimburse.
We ignore any collection problems and assume that
every revenue is just collected as such.
Hygienists appointments take the dentist less time,
they're mostly done by the hygienists,
and these visits also lead to some revenues.
Crown procedures take longer and they come with pretty significant payment.
Typically, crown procedures are associated with two visits,
one to prepare the tools,
take some measurements and put in a preliminary crown,
and the second one to put in the real crown.
At the more operational level,
we also learned about the costs associated with
the consumables as well as any lab fees. You will see the data here.
Typically, we only pay a couple of dollars per visit in consumables.
Lab fees vary with crowns probably being the biggest expense.
Now equipped with this operational data,
we can now go ahead and compute things such as a direct cost
of labor or physician hygienist utilization.
We can look at the amount per day and we can compute attack time or staffing levels,
and of course we could group process for slow diagrams and gunshots.
But, why would we do this?
What is the goal behind any analysis we could start?
I see the question, What is
the goal as a crucial difference between operations and finance?
Our life in finance is simple,
you want to make money,
and that just means that revenues have to be way bigger than your expenses.
Life and operations can be frustrating,
we have so many numbers, measures,
and metrics that we chase,
that it's easy to lose sight of the big picture.
What is missing I would argue is
a holistic perspective of what is happening in our practice.
Now how might such a holistic perspective look like?
You might be surprised to hear this from an operations professor but let
me start out by saying that ethical motives aside,
the goal of a clinical enterprise is to make money.
I know that statements like this are especially tricky in medicine.
Yes, our dentist might work to make money but also to help his patients.
I promise that the framework that I'm about to introduce is general
enough that could also help you to achieve some more our article,
but for use of illustration for now,
our focus on making a profit,
but what are profits?
And where do they come from? Here's where things get interesting.
So, let's start with profits.
Profits are revenue minus costs.
But what drives revenue?
Well, revenues come from three revenue streams: consultation,
procedures, and hygienist appointment.
Let's look at revenues from the consultations for us.
What determines the revenue from consultations?
Clearly, the number of consultations we do,
and the average money we get per consultation.
But, how many consultations do we do,
and what keeps us from doing more?
Now, talking to our dentist,
we've learned that he's working all hour.
He is capacity constrained,
and short of an occasional cancellation the dentist does not have any idle time.
This is different from the hygienist we learn, there, you know,
there was really too much work for one hygienist,
it was two one staff now we have plenty of capacity.
So, back to the number of consultations.
Since we are capacity constraints,
this is driven by the availability of the dentist,
and the average time of a consultation.
The dentist is available a certain number of days a year and a certain duration per day,
that gives us the minutes that the dentist is available per year.
A consultation we say takes time,
so if the dentist does nothing but consultations,
he could do many many consultations per year.
Now for now let's just assume the dentist spends
a certain amount of chunk of time for consultations.
So in that case, we know how many minutes a dentist is available,
and that tells us a number of consultations per year he can do.
Next look at procedures,
the logic here is similar.
We're capacity constrained in procedures because of our busy dentist.
So the number of procedures we do per year is driven by what our dentist can do.
How many procedures can the dentist do?
Now we start again with the available time per year.
We then have to take the time away that the dentist is doing consultation.
Given the minutes per procedure,
we get a total capacity of procedures per year.
Alright, one more on the revenue branch.
The hygienists revenues are driven by the number of
appointments with the hygienist times the revenue per appointment.
How many appointments do we make?
Now remember that unlike the dentists we have enough capacity for hygienist.
So we're constrained by the demand. What is demand?
With a given panel size and a given revisit frequency per patient per year,
we get the volume of hygienist appointment's per year.
Let me just pause here and observe what we're doing.
We started with a high level financial measure profits,
and then step by step we broke it up into more operational pieces,
from profits to revenues,
from revenues of the number of appointments,
all the way to operational variables such as processing time per procedure and
medical variables such as how often patients come to the hygienist appointments.
The technical term for this is we cascade
a high level financial metric into smaller operational metric.
Now let's continue this game on the expense side.
Here, we have consumables,
salaries, and overhead costs.
The consumables are directly driven by a number and the types of cases we do.
The salaries are what they are the only exception really are the hygienist,
because here we decided to staff to demand,
to the number of hygienist we hired was just so that we could fulfill our demand.
If demand double we would add capacity,
if demand crashed, we would have to take one of the hygienist and lay them off.
Now fixed costs here are just two lines, the SGA expensive,
sale general administrative expense,
and the cost of the facilities. Ready is our tree.
Now, two quick definitions we call such a tree a KPI tree.
KPI stands for key performance indicators.
KPI tree help us cascade financial numbers that you see towards the root of the tree into
operational numbers such as
processing time or other elements such as consumables per case,
that you see towards the other side of the tree.
So cascading really means taking a high level financial variable,
and understanding the operational drivers behind it.
So what makes for a good KPI tree?
Let me articulate three properties I would like to see in a good KPI tree.
The tree should be specific,
meaning it needs to be specific to the operations we study,
every operation gets its own KPI tree,
just like every operation deserves its own forces flow diagram.
Recall how we had to factor in that the dentist is capacity constraint,
was a hygienist was demand constrained.?
This is very specific unique to our context,
and it made a huge difference in the way that we design the tree.
Second, the variables in the tree need to be measurable,
we need data for the variables in the tree.
Yes, employee morale matters as well,
but I don't see an easy way of getting it into a tree.
I neither have a good measure nor do I have a functional form of
how their morale translates into something like revenues or costs.
Third, the variables in the tree need to be
actionable and that's especially true for the leaves of the tree.
I cannot tell my dentist to bring in more profits,
but I can observe that it takes him one minute
longer to put in a crown when compared to other dentist,
and that might lead to the dentist to think about the way he puts in a crown.
In this session, I introduce you to the KPI tree.
The KPI tree provides a visual link between the financial variables towards
the root of the tree and the more operational variables towards the leave of the tree.
It shows us visual that superior financial performance is driven by better operations.
Yes, the finance people get to claim the root of the tree,
but we as operations people are in control of
the leaves and that is I would argue where the action is.
After all you cannot directly impact your profits.
The way you change your profits is by tackling
something deep down the tree towards the leaves,
but where should we start?
Should we increase the panel size?
Should we shorten the processing time for a crown?
Should we renegotiate our lab fees?
The KPI tree alone helps us answer
the question but we will need the next session to
figure out how to use that. So stay tuned.
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Christian TerwieschAndrew M. Heller Professor at the Wharton School, Senior Fellow Leonard Davis Institute for Health Economics Co-Director, Mack Institute of Innovation Management
Amol S. Navathe, MD, PhDAssistant Professor of Medical Ethics and Health Policy
David A. Asch, MD, MBAProfessor of Medicine and Professor of Medical Ethics and Health Policy
Roy Rosin, MBAChief Innovation Officer
Kevin Volpp, MD, PhDProfessor of Medicine, Division of Health Policy / Professor of Health Care Management
