There is an interesting paper by Vest and colleagues titled ‘Association Between Use of a Health Information Exchange System and Hospital Admissions’.

This is an American study which looks at the use of a Health Information Exchange (HIE) in New York State. The researchers looked at a sample of 15,645 patients seen in the Emergency Department.

Here are a few reflections:

- Several Odds Ratios are identified in the paper.
- Since there are multiple comparisons there would be benefit from a correction (e.g. Bonferroni correction).
- A null hypothesis would help to discriminate between the primary and secondary analysis.
- A power calculation would identify the numbers required for the analysis
- The same-day HIE access analysis is impacted by reduced numbers in this sample set for which point 4 is relevant.

What was also interesting was that the retrospective collection of data although providing utility impacted on the questions. A key issue thrown up by the analysis is the approach to accessing the HIE in relation to decision making.

**What Does the Odds Ratio Mean?**

The odds ratios tells us about a relationship between two things. In the clinical setting these two things might be expected to have a causal relationship e.g. Blood Pressure and exercise.

**How is it Calculated?**

Taking the Blood Pressure example. Let’s suppose that a group of people have their Blood Pressure checked before and after an exercise/non-exercise intervention. Let’s also suppose that people are classed as doing regular exercise (intervention) or not doing regular exercise (non-exercise).

We want to see if there is a relationship between the two and it would be great if we could have a number to sum up that relationship. Let’s also suppose that we think the relationship is ‘Doing regular exercise reduces Blood Pressure’. The exposure is doing regular exercise and the outcome is reduced Blood Pressure.

Let’s suppose there are 100 people in the study (These are illustrative numbers only). 60 people do regular exercise and 50 people have low Blood Pressure. Of the 60 people that do regular exercise 40 people have low Blood Pressure. I will write this out in the following statements

60 people do regular exercise and 40 have reduced Blood Pressure

60 people do regular exercise and 20 have high Blood Pressure

40 people do not do regular exercise and 10 have reduced Blood Pressure

40 people do not do regular exercise and 30 have high Blood Pressure

Before we can talk about the odds ratio we need to look at probabilities and also clarify which relationship we are examining.

Firstly let us look at some probabilities.

What is the probability that someone who is in the exercise intervention group will have reduced Blood Pressure?

This would be 40/60 or 0.67 (2 significant figures). Just to explain – if someone is in the exercise group then we know that 40 have reduced Blood Pressure and 20 have high Blood Pressure. The probability is the ratio of the outcome of interest to all of the outcomes in that group. Therefore this is 40/60.

What is the probability that someone who is in the exercise intervention group will have high Blood Pressure?

The reasoning is much the same as the example above except we are substituting 20 for 40 i.e. the probability is 20/60 or 0.33 (2 significant figures).

What is the probability that someone who is in the non-exercise group will have lower Blood Pressure?

This will be 10/40 or 0.25

What is the probability that someone who is in the non-exercise group will have high Blood Pressure?

This will be 30/40 or 0.75

Now we can turn to the odds ratio which is the ratio of probabilities.

What is the odds of having high Blood Pressure with exercise compared to non-exercise.

The probability of high Blood Pressure given exercise is 0.33.

The probability of high Blood Pressure given non-exercise is 0.75

The odds ratio of having high Blood Pressure with exercise compared to non-exercise is 0.33/0.75 = 0.44

We can have several odds ratios in the same sample – it depends on what question we are asking.

What is the odds of having lower Blood Pressure with exercise compared to non-exercise.

The probability of lower Blood Pressure given exercise is 0.67

The probability of lower Blood Pressure given exercise is 0.25

The odds ratio of having lower Blood Pressure with exercise compared to non-exercise is 0.67/0.25 = 2.68.

So just to summarise the odds ratio is a ratio of probabilities. You need to work out the probabilities first. Then you need to clearly state the relationship you are interested in and then calculate the ratio of the probabilities for that relationship.

Finally just a few key points.

The odds ratio can be between 0 and infinity. So for example if all the people in the non-exercise intervention group had high Blood Pressure then the odds ratio of having lower Blood Pressure with exercise compared to non-exercise would be infinity (assuming all other figures remained the same).

The odds ratio can be affected by sample bias. So if the sample is not characteristic of the general population and is small then the odds ratio could be abnormally high or low. The confidence interval is usually calculated for the odds ratio although this can be a little complicated.

**Other Links**

There is a good explanation of the odds ratio in this paper.

**Context**

**Patient Records**

Patient records are central to the delivery of healthcare and serve a number of functions including the recording of clinical assessments and interventions. Aggregated data is also utilised at a local and national level to inform commissioning.

**Electronic Patient Records**

The digitisation of patient records offers a number of advantages over paper based records. These advantages include automated backup of records, reduced use of physical storage space (since paper based notes are switched to servers), off-site access to records using mobile devices and the potential to develop analytical clinical support tools which use computers to process clinical data to help improve clinical decisions. Not all healthcare services have electronic patient records but most providers are moving in this direction.

**Getting Electronic Patient Records to Talk to Each Other**

When patients move between healthcare providers – for instance between primary care and the hospital – they may find that one provider does not have information that the other provider has. There are many providers and many electronic paper record systems. For two systems to talk to each other they have to solve a number of problems. When these problems are solved a patient can move between providers and healthcare information can be accessed by the different providers. A key solution to this problem of health information gaps is the Health Information Exchange (HIE).

**The Health Information Exchange**

There are many definitions of what a Health Information Exchange is. (Hersh et al, 2015) define a HIE as follows:

‘**Health information exchange (HIE), the electronic sharing of clinical information across the boundaries of health care organizations’**

Whilst this definition is simple, the process of sharing clinical information between healthcare organisations is technically complex and encompasses a range of software, hardware and governance issues. The process of helping systems to talk to each other is helped by the development of standards. A set of standards is outlined in the NHS interoperability framework.

**Links to Other Posts in the Health Information Exchange Series**

General Posts to Date on Health Information Exchanges

Posts on Examples of Health Information Exchanges

SNOMED CT®/ICD Mapping and Harmonisation Posts

**Index:** There are indices for the TAWOP site here and here

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**Disclaimer: **The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog.

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