Access to Speciality Care: What can we learn from 1379 specialists in Ontario?

As a health technology company, we have an exciting opportunity to harness data and improve people’s access to healthcare. This is what every company is ultimately trying to achieve, and data is a terrible thing to waste in this endeavour. Through a series of posts, we hope to show you a little bit more about the data we collect, how we use it, and the impact it can have. In this post, we’ll show you how we update wait times.

 

Wait time data from real referrals

Every couple thousand referrals, we update the wait times, response times, and ConsultLoop score for all the specialists on our directory. How do we do it? Let’s show you.

First, we compile all the referrals made in the last 6 months across our network. For each specialist, we tally how many referrals were requested of them, how many got booked, the average wait time, and how long it takes for them to respond to a referral request. This allows us to report wait 0 (time it takes to book), wait 1 (wait time to be seen), and their response rate.

This latest round saw us collect data on 1379 specialists who received a referral request in ConsultLoop. They spanned ten LHINs, and 36 specialties.

Have a look at this map of the specialists included in this analysis.

Screenshot 2018-03-24 00.15.14

 

Of these specialists, we had enough data to provide an average wait time and response time for 825 of them. This is the information that is then updated in our specialist directory for referring clinics and their patients to consult when making a referral. This provides an updated and current directory of wait times on top of the descriptions, scope of practice, language and location information that’s already displayed and updated for each specialist. You can imagine the impact on access when we have this insight at the time a referral is being made – in fact, we often hear people hear of what we do and say we’re like the Waze of healthcare.

 

What do wait times look like?

Here is how the wait times break down.

Wait Times to see a specialist (Wait One) in Ontario

Here is the breakdown by response time reported as a decay curve – what percent of referrals are still pending by the number of days since the referral was first requested? It should be noted that these results regarding pending referrals are not generalizable – we follow up with specialists through several touchpoints in ways that a regular clinic is unable to do, so this data is likely better than what an average referring clinic experiences.
Percent of Pending Referrals by Days Since Request

As you can see we have a great many data points allowing for a statistical analysis that could not be done before. We know the average and median wait time by specialty, and which clinics perform best in replying quickly and consistently to referral requests. Our data is based on real referrals being sent through our network. In contrast, the Fraser Report on wait times is based off survey responses and recollection, and its methods have come under scrutiny by us and others. While we cover nearly 2,000 specialists in 36 specialties, Health Quality Ontario and Cancer Care Ontario capture wait times but in siloed surgical specialties. Other commercial e-referral platforms suffer in a similar way in providing a pathway to very few institutions or services.

 

How should this data be harnessed?

The question for our governments, health agencies, policymakers and researchers is how such data can best impact patient care. Can it be used to identify gaps in access, and make the case for more funding where it’s needed? Of course. But perhaps a better question to ask is how such information will be used by family doctors’ and nurse practitioners offices, when the referral is made and the patient is there. How will such centralized, updated and dynamic wait time information be used to provide patients with better access to specialty care? Data can only reach its potential value if put in the right hands, to help inform the right decisions. There is no better way to impact care than to let data influence and inform key health decisions as they happen.

How our wait time and response time data will be used on a grander scale remains to be seen. However, data like this is at the foundation of an improved, enlightened and informed health care system, and our clients who access it are part of that future.

ilanshahin

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