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What Measures?

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Designing for Sustainment: Keeping Improvement Work on Track (Patient Safety IV)

Johns Hopkins University

4.4 (23 ratings) | 581 Students Enrolled

Course 4 of 7 in the Patient Safety Specialization

Keeping patient safety and quality improvement projects on track, on time, and on budget is critical to ensuring their success. In this course, students will be introduced and given the opportunity to apply a series of tools to guide and manage patient safety and quality initiatives. These include tools for defining what success looks like, developing a change management plan, and conducting a pre-mortem to identify risks for project failure. This course will also provide tools for engaging stakeholders to ensure key players are invested in your project’s success.

Skills You'll Learn

Change Management, Project Planning, Staff Engagement

Reviews

4.4 (23 ratings)

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SS

May 23, 2018

Marvellous mentors and great course content. I would recommend this course to every health care professional at each level. Really thankful to mentors and Coursera.

NV

Jul 27, 2018

Very informative and important course for quality improvement.Thank you very much Coursera .

From the lesson

What Does Success Look Like?

In this module, learners will develop an understanding of what project sustainment is and why it is important, the potential risks to project sustainment, the importance of planning for sustainment from project inception, types of measures used in quality improvement projects and the attributes of each, how strategies for improvement were developed in two successful large-scale quality improvement initiatives. Learners will become familiar with the issues impacting sustainment and how they may be successfully addressed across the life span of a quality improvement project.

What Does Success Look Like?5:26

Failure to Sustain6:49

TRIP Plus 24:15

Planning Ahead8:41

What Measures?10:22

Examples of Successfully Sustained Projects22:00

Taught By

Lisa H. Lubomski, PhD
Assistant Professor

[MUSIC]

So what measures should we be employing?

This is something which you should plan ahead about,

you should have already established what measures you will be using and

the feasibility of collecting them before anything starts.

A sort of a general question is what are we trying to measure?

Some things related to patient safety that we might want to measure are errors,

adverse events and perhaps most pertinent here, safety targets.

Errors are, per the IOM, the failure of planned actions to be completed as

intended or the use of a wrong plan to achieve an aim.

And these include latent errors, that is defects that reside in the system,

poor design, poor staffing, active errors that are errors

made by frontline health care staff, such as dosage errors or

perhaps the incorrect performance of a task,

which might be relevant to our lecture today.

Adverse events are harm caused by healthcare.

And these, of course, are the outcomes that we might be interested in.

Safety targets include medication errors,

healthcare acquired infections, surgical complications,

device complications, patient identification errors or even death.

Some of these are processes.

Some of these are outcomes.

And we might want to measure any of these, depending on the specific

project that we are dealing with, trying to fix and hoping to sustain.

There are at least four basic methods of collecting data in general,

observation, self-report, which might be in our case interviews or

questionnaires, often of healthcare workers, sometimes patients, testing.

And physical evidence, and in most cases, this involves document review or

review of other records, perhaps.

Many documents, of course, today are no longer on paper but are electronic.

There are a number of measurement methods that we might apply,

some are prospective and some are retrospective.

Prospective methods include direct observations of patient care, and

these are sometimes used in our setting here.

Cohort studies, perhaps a bit formal for our purpose.

Clinical surveillance, which may be ideal, and we willl discuss in a moment.

Retrospective methods include record review, including of charts and

electronic medical records, administrative claims analysis.

These can be helpful, but they're a bit post hoc for our purposes.

The same is true for malpractice claims analysis.

M&M conference and autopsy results,

incident reporting systems can occasionally be helpful, again, here.

One of my favorite papers on measuring errors is from Eric Thomas and

Laura Peterson, published in the Journal of General Internal Medicine in 2003.

And this was really more of a think piece and review, but

they proposed that different kinds of errors

are best measured by different specific methods.

Latent errors, active errors and

adverse events might be best captured by different measures.

Adverse events are difficult to capture depending how frequent they are.

Some kind of active clinical monitoring may be the best way to find them.

Active errors again don't happen all that often, direct observation

might be the most useful for other types, for certain types of active errors.

Latent errors include problems with managements,

with organization, with funding.

And one can look at chart review, one can look at administrative data,

and other kinds of data including perhaps incident reporting systems,

even malpractice claims or M&Ms where many different parties weigh in,

provide their perspective and describe what happened.

So we want to think which of these methods might be most appropriate for

the project that is at hand.

Direct observation is great for active errors for

which data might otherwise be ephemeral and never available.

Direct observation can be quite precise however,

it's labor intensive and expensive.

People need to be trained at how to do it, this takes time, sometimes there are so

many things happening that the observer experiences information overload and

can't capture everything.

There may be Hawthorne effects.

That is, the observers may change what actually happens in front of them.

There may be hindsight bias.

In fact, if the observers do not record in real time and

then see that an outcome occurs, they may have selective recall and

direct observation doesn't tell you very much ever about

latent errors, those things at the blunt end of the system.

Cohort studies are a bit impractical for much of what we're talking about,

unless those cohorts can be assembled unobtrusively,

perhaps using clinical data.

And this is where clinical surveillance comes in.

Clinical surveillance is an effort that builds data collection

into the clinical workflow.

One of the best examples might be for

anesthesiologists who sit in surgical cases and

record at very regular intervals vital signs and

other key pieces of information as they occur in the course of healthcare.

Clinical surveillance be potentially very accurate and precise and

can actually also capture adverse events and what happens around them.

It can be good to test the effectiveness of an intervention to decrease a specific

adverse event as long as that adverse event occurs with some frequency.

And they can or ought to be a part of the workflow.

However these again are expensive and

again are not so good at detecting latent errors.

Chart review or I should perhaps say record review uses readily available data.

This is used commonly, and they are evolving and

developing methods to review electronic records.

Judgements from the record about whether or

not an adverse event happened may not be reliable,

that is the judgments of whoever it was was noting events that

clinician may not see them and may be somewhat biased about reporting them.

Having individual clinicians review records

is expensive because the time of those people is expensive.

Records may be incomplete and there may be missing key bits of information or

entire records may be missing.

And, again, there's often hindsight bias.

If someone viewing a record knows what the outcome was, they may see things

differently and distribute things differently than they would otherwise.

Provider surveys can be really good for latent errors.

We do not do as much of this as much as we do surveying patients,

but frontline clinicians know a lot and

can tell you a lot about their own work places and what they do.

These data also might otherwise be unavailable, for example about

the processes that are performed everyday and how well they are performed.

These surveys can capture quite a few different dimensions,

and they can cover a fairly comprehensive range of factors or events.

Here again, there's hindsight bias.

If there was a bad outcome providers might be

more likely to respond that the care was bad.

And for provider's survey to be valid you need a good or

fairly complete response rate in other to be able to say that the responses

are representative of what is actually being observed by a group of providers.

So these are advantages and disadvantages of different measurement strategies and

kind of measures.

One question is, which of these measures might be best fit to

the task that you are trying to improve and ultimately sustain?

Availability is crucial and

available measures, which can be collected without additional cost or

effort, have a much better chance of being collected and

a much greater chance of being able to be collected in a sustained way.

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