Clinical Decision Support as Nationwide Solution: Estonian Case Study

Case study

The case study will give you an overview of implementing clinical decision support nationwide in Estonia (2020). You will find in this article:

Drug-drug interactions are a significant clinical problem

Drug treatment is one of the essential tools in healthcare but is also an important reason for morbidity and mortality. Among adverse drug reactions (ADRs), drug-drug interactions (DDI) are significant and, in many cases, predictable and avoidable.

According to a Swedish nationwide study, 3.8% of patients had at least one type D drug interaction (clinically significant interaction that should be avoided), and 38% of patients had at least one type C interaction (clinically relevant interaction that dose adjustments can handle) (Holm et al., 2014).

Drug interaction checker as clinical decision support

Electronic prescribing can include clinical decision support (CDS) alerts about drug-drug interactions (DDI) and, therefore, enhance the drug-use process from prescribing to dispensing.

Electronic prescribing improves the quality of information about the patient’s drugs (active medication list) and integrates the latest evidence to e-prescription.

Clinicians study pharmacokinetics and pharmacodynamics, but without the help of technology, it is impossible to remember tens of thousands of combinations of interacting drugs. Another problem is the exponential growth of research: medical knowledge doubles every 73 days (Densen et al., 2011). How to be always up-to-date?

A clinical decision support tool for drug interactions can be integrated into electronic health records (EHR) or pharmacy point-of-sale systems (POS). This article describes nationwide adoption, whereas drug interaction service is integrated into a centralized e-prescription system. Estonia had implemented a national e-prescription system in 2010, and Lithuania had implemented a national e-prescription system in 2016.

Evidence for using clinical decision support systems

In recent years, clinical decision support systems have focused on health technology assessments (HTA). The evidence demonstrating the effectiveness of using clinical decision support systems is solid.

Evidence on effectiveness – integrating the drug-drug interaction database as clinical decision support into primary health care records was associated with a 17% decrease of serious interactions per prescribed drug-drug pair (Andersson et al. 2012).
Evidence on effectiveness – integration of the drug-drug interaction clinical decision support into e-prescription had a significant impact on prescribing habits, resulting in 28% of cases to changes in prescription by a clinician (Doogue et al. 2017).
Evidence on economic impact – implementation of drug-related clinical decision support brought a reduction of medications on average 0.82 drugs/patient (57% one drug, 11% two drugs) (Rieckert et al. 2019).

Feasibility study

Estonia implemented an e-prescription system in 2010. The wide usage of e-prescription created secondary data for research purposes.

In 2014 feasibility study “Pharmacokinetic drug-drug interactions among Estonian elderly” was done by Jana Lass, Mait Raag, and Alar Irs. A retrospective cross-sectional descriptive drug utilization study considered the Estonian Health Insurance Fund’s prescription data and was evaluated with the drug interactions database Inxbase.

All prescription medicines dispensed to Estonian older adults (>65 years old) from January 1 to December 31, 2013, were recorded. Patients who had more than two prescriptions dispensed during a predefined prescription window (≤90 days) were included. Prescription data were linked to the Inxbase database to yield the frequency of interacting combinations dispensed in the population during this period.

According to a nationwide study: 7% of over 65-year patients had at least one type D drug interaction, and 35% had at least one type C interaction.

The most common pairs of D category interactions were propafenone-metoprolol, warfarin diclofenac, and warfarin-tramadol, in 2182, 1354, and 1070 persons, respectively. D category interactions occurred most with the following medicines: warfarin (28%), metoprolol (7.4%), propafenone (7%), and clopidogrel (6.9%). 10 927 persons received one pair of C or D-category interacting medicines, 2 142 received two pairs. One person had eight pairs of interacting medications at the same period.

Read more about the database: Drug interactions database

Happy young businesswoman coach mentor leader laughing at group meeting — Happy young businesswoman coach mentor leader laughing at funny joke at group business meeting, joyful smiling millennial lady having fun with diverse corporate team people engaged in talking at work

Checklist for implementation

Active medication list – is the active medication list available that covers all drugs used by the patient, or the list includes only some of the drugs (e.g., half of the prescriptions are still on paper). We recommend using drug interactions as a nationwide service only when the e-prescription adoption rate crosses <90%.

Prescribed/purchased – is there are available data only about prescriptions or also about purchases? Adherence studies have shown that almost 20% of prescribed drugs will not be bought for different reasons.

OTC drugs – is the electronic data about over-the-counter medications (OTC) available? Studies have shown that 20% of interactions are related to OTC drugs (e.g., NSAIDs, etc.).

Drug package database – is there is an available database that covers all drugs registered in a specific country? How often is this database updated? How this data can be used – can this data be accessed via an application programming interface (API)?

Classification system – is there implemented ATC classification system by WHO?

Drug formulation – is a list of drug formulations available that cover all drug formulations registered in a specific country? How often is this data updated? How this data can be used – can this data be accessed via an application programming interface (API)?

Multisource solution – if an integrated solution uses more than one source, how will you solve the issue of duplicates? For example, some prescriptions are in central systems and some in local electronic health records (EHR).

Health registry data – There are available local health workers registry data for authentication and single sign-on (SSO) services. How this data can be used – can this data be accessed via an application programming interface (API)?

Read more compliance issues: Implementing clinical decision support in European Union

The ten most prevalent D-interactions in Estonia

No.	Substance1	Substance2	Patients	Prescriptions
1	propafenone	propafenone	1557	1254
2	propafenone + propafenone	propafenone	1254	254
3	propafenone	propafenone + propafenone propafenone	21	524

Recognition for the service

Our customer Estonian Health Insurance Fund won for drug interactions service Estonian Association of Quality Annual Award (2016) and European Quality Innovation Award in Social and Healthcare Innovations (2018).

Conclusion

Adverse drug reactions (ADRs) are a significant clinical problem, an essential reason for morbidity and mortality, and escalating healthcare costs.
Medical knowledge grows exponentially, and automation is the only way to slow down this.
Drug-drug interactions (DDIs) is a crucial clinical problem that can be prevented by clinical decision support (CDSS) to some extent.
Clinical decision support doesn’t solve all problems: often, doctors are not taking “deprescribing” seriously, there are barriers for “deprescribing” and force of habit that need to be addressed.
Clinical decision support will provide secondary data about drug-drug interactions showing the extent of the problems and create feedback loops for quality improvement.
The technical solution acts merely as a catalyst for developing an evidence-based culture in your healthcare system.