PatientLeaf has been a labour of love over the last 8 years. Being a clinician it’s the one application which I’ve been thinking about for some time and most importantly truly believe it’s made a difference to my clinical care in terms of time taken to make a sound clinical decision in the clinic and also clinical safety to understand what’s been tried before at speed which is how UX design should be.
In essence, PatientLeaf is an updated overlay which gives you a specific view of the patient based on what you are looking at during the consultation. When I see a patient they normally come with one issue at a time (although it can be several during the consultation!). The idea is we slice the medical notes to focus on this issue and present this in an easily digestible format.
We’ve taken an age-old horse-chestnut as the core of its functionality. As clinicians since the dawn of time, we’ve used medication to improve the health outcomes of patients. Though hypothesis and sheer luck at times we associate modifiable objective and measurable values with health outcomes of patients with the conditions in question. In some area’s it’s very clear eg Hba1c for Diabetes, Blood Pressure for Hypertension in other’s it’s not eg with COPD does FEV1 relate entirely to mortality?
The normal loop is to take a modifiable value -> add a drug/s to help improve this -> record the modifiable value to see if it’s improved -> add a drug/s to help improve this ….rinse and repeat until the value is within or below the ideal value based on evidence. PatientLeaf uses this concept to help the clinician to make the right choice next in the nirvana of patients taking their medication to get them to their ideal value for the least amount of drugs.
Below are some concepts which we’ve tried to address with PatientLeaf
Our first leaf is Blood Pressure and you’d think it was easy to pick up what the correct blood pressure to aim for would be. This is an entire blog by itself which I will post after this blog but as you’d appreciate it’s very condition specific eg a patient over the age of 80 would have a different optimal blood pressure for a patient with diabetes.
There are several articles around compliance of medication which can vary from 30-60% depending on several factors eg age, polypharmacy. 2 examples can be found here in Medication Non-Adherence Among Elderly Patients Newly Discharged and Receiving Polypharmacy or Changes in Antihypertensive Therapy – The Role of Adverse Effects and Compliance The 2nd article is very interesting and mentions reasons why we change medication. It’s hard to find Randomised Controlled Trials with significant numbers but compliance is a huge problem in the NHS which I will mention in another blog. How often have you had a patient in front of you who swears by taking the medication regularly and you frequently want to say “so where are you getting the drugs because it’s not from me”
PatientLeaf offers a very easy to show colour coded visual indicator of each issue so you can actually show the patient and let them make their own conclusions.
What drug to try next
A typical question I frequently ask is what drug should I try next? This is normally at the 8-minute mark when I’m thinking that we need to wrap this up as I only have 2 minutes left on average to write up the notes and call the next patient in. The next question is so what have they tried before and why did they stop it if they did? If you know current clinical systems you know they are long lists of history entries which normally take more time than you have to unpick. Unfortunately and I’m as much to blame as others we don’t code intolerances to medication unless it’s target related. So we frequently have to rely on the patient to tell us if they’ve been on the new drug we want to try before or just give it and go and make the same mistakes again.
With PatientLeaf it’s really easy to view the notes to see when you stopped medication to know why.
Photo on Foter.com
Associated Coded Information
Frequently in the current clinic notes, we have a screen with coded information which is a smorgasbord of values and data and frequently we miss out on useful information in relation to the condition we are querying. The noise and chatter of other data make us miss out what we need to know in the pressure of the consultation.
PatientLeaf data mines through the notes and lets you view what you need to know about the condition in focus for example smoking status, Qrisk, ECGs or last echo result for patients in our Hypertensive leaf.
We are still starting our journey with PatientLeaf even though it’s been a long time in concept and development. I’m at a stage that I miss it when I’m not using it and hope you do too! www.patientleaf.com