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Infoway Challenge | Dynacare Submission | CONFIDENTIAL / 13

1. Question & Team

In response to Question 1 of the Canada Health Infoway Data Challenge, Dynacare is

pleased to participate and has prepared a response centered on three use-cases which

have been chosen due to their high ordering volumes and potential clinical relevance:

I. Hemoglobin A1c

II. Thyroid Stimulating Hormone, TSH

III. Potassium

Although this particular submission focuses specifically on the tests listed above, the

associated data models can be easily extended to analyze all tests provided by

Dynacare, and on a multitude of dimensions.

A cross-functional team was created to handle this task, composed of:

Marc Chabot, Manager, Commercial Strategy and Customer Insight

Dr. Joel Goodman, VP, Strategies and Innovation

Dana Bailey, Clinical Chemist

Daniela Crivianu-Gaita, Chief Information Officer

Dorota Liszewski, Director, Informatics Solutions and Services

Goran Matic, Enterprise Architect and Data Governance Lead

2. Data Description

One dataset was used for this analysis and sourced from Dynacare’s internal Laboratory

Information System (LIS) data with the following query filters applied:

I. Patients with validated Ontario Health Card Number

II. Champlain LHIN Specimen Collection Centers (SCCS) and Client Addresses

III. Tests Entered from July 1st 2013 to June 30th, 2015 (2 years data)

IV. Test Description = A1c, Potassium, TSH

The grain of the dataset is an ‘ordered test’ with sample sizes for each test being:

Hemoglobin A1c, n=480,728

TSH, n=640,676

Potassium, n= 627,639


In addition, validated Ontario Health Card numbers are required to have a unique and

reliable patient identifier, and allow provision of any future expansion of the model.

The Champlain LHIN was chosen as the sampling area primarily due to Dynacare’s

strong market position in the region. Dynacare operates approximately two thirds of the

licensed collection centers in the LHIN, and provides community laboratory services through a number of additional collection methods. The area is also geographically

isolated from other major Ontario population centers.

This dataset excludes inpatient and non-Dynacare outpatient testing data. This does

reduce the accuracy of the analysis presented; however, in our view it does not

invalidate the conclusions or insights developed from this submission. Due to the

incomplete nature of this dataset, the repeat test-rates calculated in this study are

naturally conservative.

In addition, tests which had analytical errors were excluded from the analysis. These

errors are often due to pre-analytical process non-conformances which yields the

specimen unusable. The numbers of tests excluded from the analysis are:

I. Hemoglobin A1c, n=3,163

II. TSH, n=2,698

III. Potassium, n= 2,860

3. Analysis Method

Data was downloaded via SQL from Dynacare’s central data repository into a secure

server-based instance of Microsoft Excel 2013 with PowerPivot. All analysis was

performed within PowerPivot – utilizing the Data Analysis Expressions (DAX) language

for analysis.

The analysis method and tool does allow for integration of data from additional sources

such as OLIS (Ontario Laboratories Information Systems), or in-patient testing data

sourced from hospital networks. Unfortunately – and due to the respective time

constraints as well as privacy restriction – Dynacare was unable to obtain this additional

data.

The number of repeated tests within a forward-looking time period was calculated

utilizing a combination of DAX’s FILTER, COUNTROWS and EARLIER functions.

The formulas count the number of rows in a table filtered on the following criteria:

Valid Test Result = TRUE ()

Patient Health Card = earlier (Patient Health Card)

Ordered Test = earlier (Ordered Test)

EntryDate < earlier (EntryDate) + Y, (Y=30, 90, 120, 180)

EntryDate > earlier (EntryDate)


Each test in the dataset is considered independently.

For each test (row) the DAX expressions use a 30, 90, 120 or 180-day forward-looking

window and calculate how many times the test was repeated. Thereafter additional

metrics are written to sum the number of tests which have / have not been repeated.

The number of times a test is repeated within a time window has been considered out-

of-scope for this submission.

Test results for each row are interpreted and determined to be either ‘Normal’ or

‘Abnormal’. Abnormal results can include tests which are Low, High, or Low Critical, or

High Critical. The dimension of Normal / Abnormal presented in each use-case is based

upon the result of the current test (row), and disregards the results of previous or

repeated tests.

Only tests ordered in the first ~560 days are analyzed to see if they are repeated. Tests

in the last 180 days can only be counted as a repeated tests for those within the first

~560. This is to ensure all tests determined to have a repeat / non-repeat receive the

full 180-day window. This time window can be adjusted for 30, 90 and 120 day time

periods.

The standard deviation of repeat rates is calculated as:

√( )

√

,

…where P is the proportion, n is the sample size, and N is the population size.

Assuming that N >> n, the second term is approximately 1. Calculations shows that the

vast majority of standard deviations are < 2.5% of the repeat rate (SD / P < 2.5%).

This is further demonstrated by the smooth data trends observed in the use cases.

After observing the trends and magnitude of the standard errors, an in-depth error

analysis was considered out-of-scope for this submission.


4. Use Cases

For each use-case three standard charts (figure A, B, C) have been produced that

succinctly summarize the re-ordering patterns.

Each use-case and the associated figures are followed by the Discussion and Findings

sections – which summarize our attempts to interpret the observed ordering trends from

a clinical perspective.

Figure A: 30, 90, 120 and 180 Day Repeat Rates vs. Patient Age, Abnormal /

Normal

Summarizes the % rate of repeat in 30, 90, 120, and 180 day forward-looking windows.

Although the question states a 90-day period, Dynacare found interesting trends at

different time-window lengths.

The repeat rate is plotted against patient age, rounding to the nearest five, and if the

current test result was ‘Normal’ or ‘Abnormal’.

Figure B: 90 Day Repeat Rate vs. Patient Age and Gender, Abnormal / Normal

This chart summarizes the rate of repeat for the 90 day window.

The rates are plotted against patient age, patient gender, and if the current test result

was ‘Normal’ or ‘Abnormal’.

Figure C: Repeated and Non-Repeated Test Volume vs. Patient Age, Abnormal

/ Normal

Summarizes the total volume of tests which have been repeated – or not repeated – in a

90 day forward looking window.

The count is not of how many repeats, but how many tests have a repeat within the 90

day window. The total test volume is plotted against patient age and if the current test

result was ‘Normal’ or ‘Abnormal’.

Two charts are provided, one for male and one for female.


Use Case #1 – Hemoglobin A1c

Figure A

Figure B


Figure C

Female

Male


Discussion Hemoglobin A1c (HbA1c) serves as a time-weighted marker of average glycemic control

over the preceding two to three months, or the average lifespan of red blood cells.

Clinically, HbA1c is used both to diagnose type 2 diabetes and to monitor treatment

effectiveness. For the former, the Canadian Diabetes Association (CDA) recommends

measuring HbA1c every three years in individuals ≥40 years of age or in those at high

risk.

In the absence of overt symptoms of diabetes, repeat measurement of HbA1c or an

alternate screening test is required for the diagnosis of diabetes. For the latter, HbA1c

measurements should be performed every three months when glycemic targets are not

being met and/or when diabetes therapy is being adjusted or every six months (~180

days) when glycemic targets have been consistently achieved. Therefore, the

expectations for repeat results are as follows:

1) Abnormal HbA1c values may be repeated within a short time frame (<30 days) if

used for diagnostic purposes. Normal HbA1c values should not be repeated within a

short time frame (<30 days) unless results are inconsistent with the clinical picture.

2) In diabetic patients, HbA1c testing should be repeated every 90 if clinical targets are

not being met (i.e. the HbA1c is abnormal) and every 180 days if clinical targets are

being met (i.e. the HbA1c is normal).

Findings

Analysis of HbA1c testing patterns indicate that both the absolute number of HbA1c

tests requested as well as the percentage of repeats per age partition increased with age

with no differentiation between gender.

This is consistent with the age of onset of type 2 diabetes and CDA screening

recommendations.

Encouragingly, although the majority of test results were normal, the majority of

repeats were performed on initially abnormal results. Consistent with the expectations

described above, within 180, 90, and 30 days , 45-65%, 20%, and <5% of abnormal

HbA1c results were repeated, respectively; conversely, only 5-25%, <10%, and ~1% of

normal HbA1c results were repeated within the same timeframe.

In general, these findings support adherence of physicians to the CDA guidelines for the

diagnosis and monitoring of type 2 diabetes.


Use Case #2 - Thyroid Stimulating Hormone

Figure 2-A

Figure 2-B


Figure 2-C

Female

Male


Discussion

Thyroid disorders, including hypothyroidism and hyperthyroidism, are mostly commonly

found in women with an age of onset between 30 and 50 years of age.

Measurement of thyroid stimulating hormone (TSH) is currently the recommended initial

screening test when thyroid disease is suspected and follow-up test for monitoring of

thyroid treatment. Since TSH values change slowly, frequent repeat testing of TSH is

considered unnecessary. When monitoring hypothyroidism, TSH should be tested after

45-90 days following a change in replacement hormone or clinical status.

Similarly, when monitoring hyperthyroidism, TSH should be tested after at least 90 days

post-treatment initiation.

Findings

Examination of the TSH ordering patterns indicate an increase in the absolute number of

tests ordered according to age but a relatively constant percentage of repeat tests.

Abnormal test results were repeated at a rate of 60-70% within 180 days, 40-50%

within 90 days, and ~10% within 30 days; normal test results were repeated at a rate

of 20%, 5%, and ~1% within the same timeframe. In accordance with the increased

prevalence of thyroid disease in females, a higher percentage of repeat testing was

observed in females versus males.

In total, this suggests that in the majority of patients for which TSH is monitored,

repeats are performed within the recommended 90 day window.


Use Case #3 - Potassium

Figure 3-A

Figure 3-B


Figure 3-C

Female

Male


Discussion

In a general outpatient setting, potassium is monitored in individuals receiving various

pharmacotherapies such as blood pressure medications (e.g. ACE inhibitors) and

diuretics (potassium-sparing or potassium-wasting).

In more specialized instances, potassium may be monitored as an adjunct test when

assessing kidney function, chronic heart failure, and other conditions causing electrolyte

abnormalities.

To our knowledge, for potassium, there are no specific guidelines provided to general

practitioners regarding the appropriate frequency of ordering.

However, clinicians are advised to measure potassium when initiating or modifying an

ACE inhibitor or diuretic drug regimen, or every 90-180 days when treatment is

stabilized.

Findings

The data from Dynacare indicates that the majority of potassium test results are normal,

that the number of test requests increases with age group, and that the majority of

repeat tests are requested on previously normal values.

As a relative percentage, 40-60% of abnormal potassium results are repeated within

180 days, 30-50% within 90 days, and 20-30% within 30 days; conversely, 10-40%,

10-20%, and 5-10% of normal potassium results are repeated within the same

timeframe.

Due to the lack of guidelines for potassium ordering, we cannot conclude on the

appropriateness of the repeat ordering behavior observed.

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