FormalPara Key Points

A medication optimization virtual interdisciplinary geriatric specialist (MOVING) programme is feasible.

This retrospective study of the MOVING programme found no change in the number of medications or polypharmacy; however, it found improved prescribing among older adults with multimorbidity. While preliminary findings suggest that this model of care decreased unintended hospital visits, larger pragmatic trials to study the impact on emergency department visits, falls and mortality are warranted.

1 Introduction

Hospitalizations for adverse events (ADEs) occur in hundreds of thousands of older adults each year [1,2,3]. Specifically, although older adults comprise 14.2% of the Canadian population, they account for 57.6% of ADE-related hospitalizations in Canada [3]. Moreover, the consequences of ADEs are greater among adults with advanced age which include an increased risk of death; hospitalization, with those over the age of 85 being at least three times more likely to require hospitalization for an ADE than those between the ages of 65–69; and healthcare system costs [1,2,3]. Because of the individual- and system-level impacts of ADEs and the largely preventable nature of these events, interventions that can mitigate the clinical and economic burden of ADEs among older adults are needed [4,5,6].

De-prescribing, the planned and supervised process of dose reduction or withdrawing medication that may be causing harm or is no longer of benefit, is an increasingly adopted strategy for decreasing the risks associated with polypharmacy among older adults [4, 7,8,9]. Findings from systematic reviews suggest that de-prescribing is safe and associated with reduced risks of falls and mortality [4]. However, widespread implementation of de-prescribing in primary care settings can be undermined by patient- and system-level barriers that include lack of time, incomplete medication histories and lack of guidelines [4, 10,11,12,13]. Lack of access to expertise in geriatric medicine and psychiatry, typically concentrated in large academic centres, is another potential barrier to successful de-prescribing [14, 15]. Providing access to such expertise through telemedicine or virtual care platforms may facilitate de-prescribing and optimize pharmacotherapy for older adults [16,17,18,19,20].

A medication optimization virtual interdisciplinary geriatric specialist programme (MOVING) with geriatric clinical pharmacology, pharmacy and psychiatry expertise could support clinicians in optimizing pharmacotherapy for older adults with evidence-based recommendations [21,22,23]. This MOVING model of care is delivered virtually to facilitate rapid coverage for the whole province of Ontario, Canada, an area with insufficient numbers of geriatricians and geriatric specialists to support its ageing population [15, 21]. However, whether the MOVING model of care can improve prescribing among older adults remains unknown. We sought to determine whether MOVING facilitates de-prescribing and reduces the use of potentially inappropriate medications in older adults with polypharmacy.

2 Methods

2.1 Study Design and Participants

We conducted a retrospective before-and-after study. We obtained consecutive electronic charts from consultations from GeriMedRisk, a publicly funded example of MOVING, which supports the province of Ontario, Canada, from hospital, primary care, long term care, or other outpatient clinics, from 1 June to 31 December 2018. We included charts for patients aged 65 years and older. We excluded patient charts with incomplete data (e.g. unknown patient age, illegible chart, unavailable medication list) because we could not reliably conduct pre- and post-consultation comparisons. This study was reviewed by the Hamilton Integrated Research Ethics Board as a programme evaluation; therefore, no formal ethics review was required. This study was reported using the CONSORT GUIDELINES, with an added extension for randomized pilot and feasibility studies (supplementary information).

2.2 Data Sources

We de-identified and anonymized patient charts from GeriMedRisk prior to chart review to extract data. We captured medication history, medical comorbidity, and demographic information from this dataset. We used the same dataset to capture programme metrics such as wait times. This dataset has been validated for completeness and accuracy (supplementary information). This data is stored on servers of GeriMedRisk’s Health Information Network Provider, the Canadian Mental Health Association Waterloo Wellington in Guelph, Ontario. Finally, to explore the impact of these prescribing recommendations in general and on hospital visits in the 6 months following the intervention, we attempted to contact all the referring clinicians (physicians, nurse practitioners) of these included charts and conducted semi-structured interviews with a convenience subset who responded for this sample.

2.3 Interventions

MOVING is a virtual consultation service comprising clinicians from the following services: pharmacy [pharmacist with Geriatric Pharmacy Specialty Certification (BCGP)], geriatric medicine (board-certified specialist physician in internal medicine and geriatric medicine), clinical pharmacology (board-certified specialist in internal medicine and clinical pharmacology), and geriatric psychiatry (board-certified specialist in geriatric psychiatry). Physicians, pharmacists or nurse practitioners who worked in Ontario, Canada, and identified a need for medication optimization by one of or a combination of the above services, could request a MOVING consult from GeriMedRisk through fax, phone, asynchronous virtual care (eConsult) via the Ontario Telemedicine Network or BASE eConsult. Upon receipt of the consult, the MOVING pharmacist would ensure that consent from the patient for their information to be shared with MOVING was obtained from the referring clinician. Following consent, the MOVING pharmacist would conduct a Best Possible Medication History (BPMH), a systematic approach to obtaining a complete history of the patient’s prescription and non-prescription medications, natural health products, alternative medicines, and use of alcohol, cannabis and other recreational substances. The BPMH would be compiled from a pharmacist-led interview with the patient or, with consent, designated caregiver, their pharmacy records and provincial records of pharmacy dispensing available through the Digital Health Drug Repository. The MOVING pharmacist would also triage the patient consult for urgency and determine which specialist physician services were necessary, depending on the nature of the referral, the patient’s comorbidities and medications [21, 22]. MOVING specialist physicians provided asynchronous virtual care. Each MOVING clinical specialty service would review the available clinical information provided by the referring clinician, patient and/or caregiver, and from clinical notes available through the provincial clinical record repositories Clinical Connect or ConnectingOntario. Each speciality would provide their recommendations and involve other specialty services as needed. Following specialist physician assessment, the MOVING pharmacist would synthesize all recommendations while reviewing drug–drug interactions and feasibility of implementation (e.g. cost to patient, barriers to adherence, etc.). Disagreements were resolved and any necessary modifications to the MOVING consult were made through inter-professional communication within the MOVING team. The integrated multidisciplinary MOVING consult would be sent back to the referring clinician accompanied by relevant geriatric drug information through the original mode of referral [21, 22].

MOVING personnel included a clinical pharmacist full time equivalent (FTE) of 0.8, geriatric specialist physicians FTE 0.5 for geriatric medicine and/or clinical pharmacology, FTE 0.5 for geriatric psychiatry, and office support FTE 1.0. In addition to MOVING consults, the team held weekly 1-h drug summary rounds to review in-depth the geriatric pharmacology of specific prescription and non-prescription drugs as internal capacity building, and ad hoc inter-professional conferences to resolve any recommendation conflicts.

2.4 Study Outcomes

Our primary outcome was the prevalence of polypharmacy (defined as ≥ 4 medications per patient) [24], and the change in the total number of prescribed medications as recommended by the intervention, a MOVING consult, which included medications prescribed for regular and as needed use. Although there are various definitions of polypharmacy in the literature, we selected the cut-off of ≥ 4 medications per patient to align with other health system interventions in Ontario and as an indicator of moderate to severe polypharmacy [11, 24]. In secondary analyses, we quantified the change in regularly administered and as needed prescriptions separately. We also examined changes in prescriptions for medications commonly associated with harm in older adults, including long-acting opioids and psychotropics (i.e. sedative hypnotics, antidepressants, anxiolytics, antipsychotics, lithium, antiseizure medications). We included anti-diabetic medications as a non-psychotropic medication. Finally, we defined changes in potentially inappropriate medications by comparing Beers and Screening Tool of Older Persons’ potentially inappropriate Prescriptions (STOPP) scores before and after a MOVING consult [2, 6, 25, 26]. The Beers Criteria is an evidence-based list of drugs that should be avoided in older adults [26]. We derived a Beers score by assigning a score of 2 to medications which should generally be avoided in all older adults (i.e. absolute recommendation to avoid) and a score of 1 for those medications which are recommended to be avoided in patients with specific diseases or conditions (i.e. conditional recommendation to avoid). Similarly, we derived a STOPP score by assigning scores of 2 and 1 for each medication which definitely and possibly meets STOPP criteria, respectively [25]. Importantly, while the MOVING clinical team members were aware of the STOPP/Screening Tool to Alert to Right Treatment (START) and Beers criteria, they did not restrict their assessments or recommendations to these criteria and conducted comprehensive medication assessments and provided optimization recommendations that considered all facets of the patient’s circumstances. Examples of medication review recommendations made through the MOVING programme’s intervention included discontinuation of medications with no benefit, with duplicate effects, with no evidence to support benefit, and with harmful adverse effects in isolation or through a drug–drug interaction. Medications with a strong indication and benefit may be switched to an alternate medication with a better safety profile (e.g. to treat depression in a patient experiencing cognitive impairment, use sertraline instead of paroxetine due to fewer adverse anticholinergic effects and less potential to cause a pharmacokinetic drug–drug interaction). Pharmacists would also consider physical or cognitive barriers to improve adherence to medications (e.g. check inhaler technique, decrease pill burden, review bioequivalence of medications if crushed for dysphagia). Medication dose changes, initiation of evidence-based medications or investigations to guide medication optimization would also be made within the scope of each MOVING clinician’s scope of practice. In preparation for this study, we determined that extraction of all required data from electronic charts was feasible (Supplementary information) [22, 27]. Data collection was performed independently and in duplicate until inter-rater reliability, defined a priori as a Cohen’s kappa greater than 0.8 and a Pearson’s coefficient greater than 0.824, was achieved [27,28,29] (Supplementary Information). After agreement was achieved, all of the charts were evaluated with 10% performed in duplicate. We calculated wait times for the intervention by subtracting the number of business days between the date the referral was received and the date the MOVING consult note was sent back to the referring clinician. We used the Fraser Institute’s Waiting Your Turn: Wait Times for Health Care In Canada, 2018 report as a reference for specialist wait times [30]. We included hospital diversion as a result of the intervention as a secondary outcome. With a semi-structured phone interview, referring clinicians were asked if they had considered sending their patient to hospital before the MOVING consult, and of those patients who were possible hospital transfers, how many still went to hospital and how many avoided a hospital visit. We considered consults who were diverted from hospital only if the responding clinician solely attributed the hospital diversion to the recommendations provided by the MOVING consult. We also inquired about patients who required a hospital visit as a result of the MOVING consult. Costs per patient included reimbursement for fee-for-service for specialist physicians (in-person versus virtual care), salary for non-physicians, programme costs, and care partner logistics (time taken off work to accompany patient to appointments, parking during appointments). Estimated hospital admission costs at the time of this study were derived from the 2016 annual report of the Office of the Auditor General of Ontario and the Ontario Ministry of Health and Long Term Care Speciality Psychiatry Hospital Services and the Canadian Institute for Health Information Cost of Acute Care Hospital Stays by Medical Condition in Canada 2004–2005 [31, 32].

2.5 Data Analysis and Sample Size

All data analyses were completed using SPSS v26.0 or Microsoft® Excel®, 2016. We conducted a descriptive analysis of patient demographics and care setting. For our primary outcome, we determined that we would need a minimum sample size of 34 charts to detect a moderate effect size of 0.5 with 80% power. We compared changes in all outcomes using paired t tests. We considered a p value of 0.05 as statistically significant, and used the Bonferroni-corrected p value of 0.006 to determine statistical significance for our prescribing outcomes [33].

3 Results

Between 1 June and 31 December 2018, there were 55 referrals. Following exclusion of 15 (30.9%) charts because patients were under the age of 65 years (n = 11) or due to incomplete data (n = 4), we included 40 patients in the analysis. The mean age of patients was 80.6 [standard deviation (SD) 8.8] years and 25 (62.5 %) were female (Table 1). All patients had polypharmacy before and after the intervention. The mean (SD) total number of prescriptions per patient before and after the MOVING consult was 12.02 (5.83) and 11.58 (5.28), respectively. The mean difference was −0.45 (95% CI −0.94 to 0.04, p = 0.07), which did not reach statistical significance.

Table 1 Baseline characteristics (n = 40)
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Overall, the intervention did not result in a significant change in the number of standing medications (mean difference −0.05; 95% CI −0.52 to 0.42, p = 0.83), prescriptions for psychotropics (−0.40; 95% CI −0.72 to −0.08, p = 0.02), long-acting opioids (−0.10; 95% CI −0.20 to 0.00, p = 0.04) or diabetic medications (0.0; 95% CI −0.07 to 0.07, p = 1) (Table 2). In contrast, prescriptions for as needed medications per patient decreased following the intervention (mean difference −0.30, 95% CI −0.45 to −0.15, p < 0.001), as did both the Beers (−1.25; 95% CI −2.00 to −0.50, p = 0.002) and STOPP scores (−1.65; 95% CI −2.33 to −0.97, p < 0.001) (Table 2). From the convenience sample where hospital diversion outcomes were available, there were 6 (15%) patients whose clinicians attributed the MOVING programme’s recommendations to hospital diversion. Descriptive analysis showed a trend towards decreased Beers and STOPP scores in these patients. No patients required hospitalization as a result of the provided prescribing recommendations. The median (range) wait time for the intervention was 5 (1–6) business days. Excluding the costs saved from hospital diversion (range $18,458 for an acute medical stay for dementia to $55,800 for an inpatient mental health stay for 60 days at a cost of $930/day) [31, 32], the cost of the MOVING programme was $629.80 per person involving pharmacy, geriatric psychiatry and geriatric medicine/clinical pharmacology compared with in-person consults involving the same number and type of specialities at a cost of $1270.69 per person with a total wait time of at least 16 weeks [30]. For a consult involving pharmacy, and one specialist physician (geriatric psychiatry, geriatric medicine/clinical pharmacology), the MOVING cost was $545.80 per person compared with the traditional in-person cost of $904.89 per person.

Table 2 Prescribing outcomes of a medication optimization virtual interdisciplinary geriatric specialist programme
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4 Discussion

Virtual care, or telemedicine, is an important emerging method of healthcare delivery. Certainly, in the current era of the coronavirus disease 2019 (COVID-19) pandemic, telemedicine has become more commonly used to support older adults with multimorbidity and frailty [16, 17, 23, 34,35,36]. In this study, we found that the MOVING model of care was associated with a statistically significant decrease in prescriptions for as needed medications and potentially inappropriate medications for older adults, represented as decreases in Beers and STOPP scores. However, we found no change in polypharmacy or the total number of medications and prescriptions for psychotropics, long-acting opioids and diabetic medications, even though the suggestions suggested a decrease. It is possible that the number of medications remained unchanged since an alternate medication with less harm to the patient was suggested. For example, use of standing acetaminophen for osteoarthritis pain, instead of a non-steroidal anti-inflammatory drug, could provide analgesia with fewer cardiovascular, renal and gastrointestinal adverse effects. Together, these results suggest that MOVING may reduce ADE-related harm to older adults by reducing inappropriate medications identified by the Beers or STOPP criteria, as opposed to strictly decreasing the number of a patient’s medications.

Our study builds upon earlier research examining the role of telemedicine interventions to optimize prescribing in older adults [17, 18, 34,35,36,37]. Specifically, although existing studies have found that chronic disease management and access to specialists can be facilitated by telemedicine, ours is the first evaluation of an interdisciplinary intervention comprising expertise in geriatric psychiatry, geriatric pharmacy, geriatric medicine and clinical pharmacology. In contrast to our expectations, there was no statistically significant change in the total number of prescriptions before and after the intervention. However, a change in the total number of prescriptions may not capture the full impact of MOVING given that many referrals involved new or worsening medical or psychiatric problems, for which new prescriptions were indicated to prevent harm. As such, withdrawal of medications considered potentially inappropriate may be a more appropriate and meaningful indicator of the effectiveness of geriatric pharmacotherapy interventions such as MOVING, as these drugs have been associated with harm in older adults. Our preliminary analysis of hospital visit diversion supported this hypothesis. The virtual nature of MOVING also allowed for rapid service delivery at less cost to a complex and vulnerable population located across a large geographic area by a small interdisciplinary team of specialists in medication optimization. This model was able to divert patients from the hospital and may be helpful to other areas where a limited number of health care professionals need to rapidly serve a large population with medical complexity.

Our study has limitations. A proportion of charts were excluded due to missing data. The cause for this was identified to be related to errors with data input at the time of consultation and has been rectified with subsequent quality improvement initiatives. Although we achieved our target sample size, our sample size was small and we cannot exclude the possibility of a type 1 error for outcomes that attained statistical significance. Moreover, because the intervention was implemented on a province-wide basis, randomization of individuals or sites to receive the intervention was not possible. Our primary outcomes were the number of medications, polypharmacy and potentially inappropriate medications, which may be considered surrogate endpoints to adverse drug events. Whether these endpoints are associated with reduced morbidity and mortality is unknown. A 2018 systematic review of 32 studies involving interventions to improve medication appropriateness found unclear benefit [38]. Another limitation of our study included the inability to capture medication dose adjustments or changes in psychotropic agents to safer or more effective alternatives which may have been clinically significant. The latter, however, may have been detected by our use of validated medication appropriate scores such as the Beers and STOPP/START. Although we studied the impact of MOVING on hospitalizations and costs, our findings are limited by convenience sampling for the semi-structured interviews conducted to explore these outcomes and the associated potential for selection bias. Consequently, our findings should be considered preliminary and lend support to further quantitative and qualitative evaluation impacts of the MOVING model of care on mortality, morbidity and health care utilization. Lastly, even though complete adherence to the MOVING consult’s recommendations could not be confirmed, a previous feasibility study found that adherence was strong [39].

5 Conclusion

We found that an interdisciplinary geriatric clinical pharmacology, psychiatry and pharmacy virtual consultation service was feasible and it resulted in decreased as needed and potentially inappropriate prescriptions for older adults. We found no significant change in total number of prescriptions, number of standing prescriptions, psychotropics, long-acting opioids or diabetic medications. Overall, this study provides promising evidence that a MOVING model of care is associated with decreases in potentially inappropriate medications. The effect of MOVING involvement on other patient-important outcomes such as falls, emergency department visits, and ongoing medication appropriateness should be investigated with larger pragmatic trials.