metrics healthcare

7/27/2019 Metrics HealthCare

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HEALTHCARE METRICS


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About Metrics

Metricis derived from the word

measure.

Whatever metric you decide to use will

depend on the process and whether datacan be obtained at steps within thatprocess

Metric should be meaningful andrepresentative enough to judge work,effort, quality, timeliness, and satisfaction.


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Metrics

Healthcare metrics are usually in the form oftime, count, proportion, costs and evaluation.

Although metrics are made for a number ofreasons including control, improvement and

compliance, we will focus on improvement.


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Example Metrics

Time

Length of stay days

Time in waiting rooms

hours Time to process a claim

Time spent assembling patients charts


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Example Metrics

Count (Total or Average)

Number of readmits

Number left without being seen

Number of patients with incomplete lab slips

Number of patients contacted

Number of errors occurring in a procedure

Comparative count data


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Example Metrics

Proportion

% Complications

% Readmits

% Understaffed

Costs

Cost per case or patient

Total costs of procedures Total cost of salaries

Savings per case


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Example Metrics

Evaluation

Customer surveys

Customer complaints


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Metrics Measuring Improvement

Improvement, as measured with data, is

based on comparing for differences in the

averages of populations or differencesfrom some standard or target.

And on comparing for statistical

differences in variation dispersion

(distribution) about means.


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Metrics Parametric vs

Nonparametric Analysis

Tests for differences in averages are either

parametric or nonparametric.

Parametric tests are considered more powerful in

measuring differences in means and can be used

to measure differences in the amount of

dispersion.

Nonparametric tests can be used to compare

non-normal distributions.


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Parametric (Normal) Distribution

100 %

95 %

68 %

-3 -2 -1 +1 +2 +3


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Metrics Parametric Restrictions for

Differences in Means

The data must be approximately normallydistributed.

The variances (measures of normalvariation) must be equivalent, at least toan acceptable degree.

Note: Parametric testing is more restrictivethan the non-normal counterpart.


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Metrics About Nonparametric Tests

Nonparametric tests are suitable for comparingdata from non-normal distributions.

Nonparametric tests rank the data by order ofmagnitude and compare the medians rather thanthe means.

Small samples from suspected non-normalpopulation distributions can be compared bynonparametric testing.


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AMI Length of Stay

0.00

10.00

20.00

30.00

40.00

50.00

60.00

1.00 2.00 3 .00 4.00 5.00 6 .00 7.00 8.00 9 .00 10.00 11.00 12.00 13.00 14.00 15 16 20 21 23 25 29 33 34

Length of Stay - Days

NumberofPatent

Stddev 5.21

Max 34.00

Min 1.00

Mean 5.25

Median 4.00

Mode 2.00

Nonparametric


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Metrics Frequencies

Chi-square tests for analyzing frequencies arevery useful for things that are counted andclassified on nominal scales (categories) such assex, age-group, blood group type, ethnic originand so on. Tests commonly used are:

1. Test for Homogeneity

2. Goodness of fit

3. Test of association Contingency tables


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Metrics Sample Size

The larger the sample size, the smaller the

differences in averages that can be detected.

Often, the amount of difference in averagesthat is desired to be detected is balanced

against the size of the sample required for

this detection and the amount of error that

can be tolerated for this detection.


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Metrics Sample Size

However, very small differences in

averages can result in large savings in

costs, thus making large samples

necessary.

On the other hand, there are diminishing

returns as the sample size becomes largerand larger.

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