The Electronic Medical Record becomes smart with the assisted prescription

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Following the interest raised with the article of widget concept in the EMR designing, let’s now look at the medicine prescriptions and how it can be ameliorated and made more useful.

A study conducted in UK in 2000 says that the adverse events due to mistakes during a “medicine therapy” are the main recurring cause of damages to hospitalized patients, representing about half of the registered events.

Another survey conducted in 1,116 United States hospitals, evidenced that medication errors occurred in 5.07% of the patients admitted each year to these hospitals.
A recent survey conducted in the United States showed that the majority of patient errors leading to adverse events occurred in administering the medication and often involved hypoglycemic medications (28.7%), cardiovascular medications (21.7%), anticoagulants (18.6%), or diuretics (10.1%).

The EMRs implement this function through the medicine selection, by name, active ingredient or equivalence group, dosage planning and therapy duration. Checks are managed through an advice or block when the doctor prescribes a medicine listed among the patient’s allergies, and an alert for possible interactions among medicines. These interactions come from the medicines catalogues used by the EMR and sometime are partial or not classified per severity, so resulting few credible and useful.

How can a function really assist the doctor in choosing and prescribing a medicine? Once again I would suggest a concept which is only aimed to describe and make real those concepts to be followed during the designing of a smart EMR. Do not pay attention to the form but to contents of the graph.

Let’s simulate a real case and imagine you must prescribe a drug to a 79-years old woman who is hospitalized for a suspected atrial fibrillation. The lady had a previous myocardial infarction and suffers of osteoporosis. She follows a therapy with lorazepan, acetylsalicylic acid, pantoprazole, alendronic acid, citalopram. In the past she was diagnosed an allergy to esomeprazole. Visiting the lady, the doctor notes the symptoms of an atrial fibrillation and decides to prescribe her the amiodarone.

When the drug is selected, the prescription function shows a full picture of information to consider before confirming the prescription choice.

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Under the box of selected drug, on the left, the suggested and limit dosage for the pathology. Below, another box describes the pharmacological risk of prescribing amiodarone can occur: an interaction to avoid (D2) with citalopram, included in the patient’s therapy; a contraindication when the patient is old; a minimum risk of hepatic distress; a moderate risk of bleeding (bleed icon) and a high risk of torsade de pointes (graph in the red frame).

On the left, under the pharmacological risk box, a list of alternative drugs is shown, with relevant risks in relation to the actual therapy (above on the right). Risks are pointed out with icons. The therapy list highlights the interaction of citalopram (in red) with the amiodarone, and a potential hypersensitivity risk between pantoprazole (in orange) and esomeprazole present in the allergies list. The hypersensitivity derives from the chemical similarity between esomeprazole and pantoprazole which in this case should keep out the use of the last one.

Under the allergies box the system shows a list of examinations to be prescribed before beginning a therapy with amiodarone, since it can cause hepatic, thyroid and respiratory problems. Right of the allergies box there is a list of significant parameters and below a box with additional drugs which should be prescribed in relation to the patient’s entire clinic picture (in this case the use of clopidogrel together with the aspirin after the myocardial infarction and the treatment with an anti-thrombosis).

The interface should permit to access the detailed information, with a simple touch on the box, so allowing doctors to rightly evaluate the risk of supplied information.

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Where can the information be taken from? And how much reliable are they? To obtain this result it is necessary to integrate the prescription function with a Medication Decision Support System (MDSS), a drug prescription specialised system, able to provide Evidence Based Medicine information.

The integration should be considered and analyzed in the EHR designing phase. First all implications a drug prescription can have must be foreseen: clinic risks as bleeding, torsade de pointes, lack of equilibrium, anticholinergic effect, constipation and so on; the need to prescribe examinations to confirm the choose or monitor some hematic or vital parameters, as well as the prescription of additional drugs. In other words, a reference information model for the drug prescription and its treatment is mandatory.

After that, a dashboard shall visualize one or more MDSS resources and information to let doctor read the relevant contents. At last, other functions shall be integrated, such as the tests prescription, as to turn suggestions into actions (under doctor’s confirmation).

A prescription function conceived in this way possess an intrinsic value higher than the actual available functions, which are limited to document (on-line register) the doctor’s choices, with a low significant improvement in respect to paper.

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