Module 3: What Makes Health IT Hard?

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Health Care IT: Challenges and Opportunities

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Icahn School of Medicine at Mount Sinai

Health Care IT: Challenges and Opportunities

122 classificações

A strong argument can be made that the health care field is one of the most information-intensive sectors in the U.S. economy and avoidance of the rapid advances in information technology is no longer an option. Consequently, the study of health care information technology and systems has become central to health care delivery effectiveness. This course covers the modern application of information technology that is critical to supporting the vision and operational knowledge of the health care leaders in managing the health care organization. Heath care decision-makers have to meet head-on the dynamic challenges of health care delivery quality, cost, access, and regulatory control. Additionally, this course integrates the Healthcare Information System as integral to the Quality Assurance Tracking Programs including measurement of systems inputs, processes, and outputs with special emphasis on systems outcomes research and organizational accountability to its various stakeholders, not the least of which are government regulators.

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Module 3

Module 3: What Makes Health IT Hard?5:05

Conheça os instrutores

Bruce J Darrow, MD, PhD
SENIOR VICE PRESIDENT MSHS IT | ASSOCIATE PROFESSOR of Cardiology & Population Health Science and Policy
Cardiology
Nicholas G Genes, MD, PhD
ASSOCIATE PROFESSOR | Emergency Medicine ASSOCIATE PROFESSOR | Genetics and Genomic Sciences
Emergency Medicine

[MUSIC]

Hi again, this is Bruce Darrow and

the topic is examples of What Makes Health IT Hard?

So I'm going to use two examples here, one to start for

comparison of what information sharing looks like in the banking

Industry where the data is very well describe in the standards to exist.

The financial data example is how many dollars you have in the bank.

And I'm going to list the scenario as follows.

Let's say that in January, you have $100 in bank A and

you're going to deposit to deposit $200 in bank B and $500 in bank C.

Then in June, you come back to bank B and you deposit an extra $500.

You close your account at bank A and

put the $100 that used to be in bank A in bank C.

Then in October, you go back to bank C and you withdraw $200.

Let's what see what would look like if you go to different banks.

Because the information is shared immediately as needed and because

the value of a dollar is well-defined, no matter what bank you go to in January,

all of them are going to show the same balance for you, which is $800.

When you come back in June and you make a $500 deposit to one of your banks and

move your money around with the other two, it doesn't matter.

The total amount of money at all of your banks is going to be the same at $1,300.

And when you go back after taking out $200 in October, you're going to be at $1,100.

At no point will you be able to take out more than the sum total of all your money

at those banks from any of your locations because they have the ability to check

with one another.

And to make sure that they have an accurate sum total at any given time.

Let's compare this now with an example of how different

electronic medical record systems might take account of a patient's allergies.

For this example, I'm going to describe the following.

In January, a patient might go to a doctor and say,

I have no allergies to medications but I am allergic to cats.

And when I was prescribed a medication called lisinopril,

I had a side effect that made me cough a great deal.

In June, after taking some other medications,

the patient may come back and say, I had some bad reactions to medications and

now I have allergies to both penicillin and aspirin.

In October, the patient might go back to either the same doctor or

a different doctor and say, well, I went through a process to

desensitize my allergy and so I'm no longer allergic to aspirin.

If you now think about how your allergy record might look in different doctors'

offices based on this same information, here's what it might look like.

In January, one doctor hearing this information might say,

you have no allergies.

A second doctor might say, well, Lisinopril is a side effect but

I'm going to note it here as an allergy so that I can get a warning any time I or

somebody else tries to prescribe it to you.

Also, I'm going to put down your allergy to cats.

Even though it's not a medication, it is an allergy that you're reporting and

I'm going to put it as part of your record.

Then in June, when you go back and you have the additional described allergies

to aspirin and penicillin, you might have both of those allergies listed.

But you might have one doctor who also lists an allergy to Lisinopril and

another doctor who lists an allergy to cats.

Then in October, you might have one doctor who lists cats, Lisinopril and

penicillin and another doctor who only lists penicillin.

So these were six different doctors offices within the period of a year and

six different allergy lists for the same patient.

And there's no really good way for them to keep an accurate running

total at any given time of what your true allergies are because there

are new allergies being added, old allergies going away.

And then there's information that some offices might consider allergies and

other offices might not.

So, if you think about this as only one of the many examples of data types within

in the healthcare industry for which there is no standard definition.

You begin to see the kind of complexity that is involved

in maintaining accurate healthcare records for

a single patient across multiple episodes and locations of care.

[MUSIC]

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