Discussion
This study provides novel evidence that ward doctors can identify
patients at risk of medication-related harm resulting from PEs. Our
findings demonstrate that when clinical ward physician patients selected
patients a for a CMR, a significantly higher proportion of these
patients were identified with clinically relevant PEs compared to
patients in surgical and medical wards where no selection was applied
(p<0.001). This effect remained significant after adjusting
for confounding in our matched case-control design. These results
support the hypothesis that ward doctors can play a crucial role in
identifying patients at risk of potential medication-related harm.
Prescribing in the in-hospital setting is associated with PEs (29, 30).
The initiation of new medical treatment and adjustment of existing
regimens can introduce unanticipated drug-drug interactions, adverse
drug events, or unintentional discontinuation of chronic medication
(31). Conducting a medication review can help reduce the number of
drug-related problems and minimize harm associated with inappropriately
prescribed medication. However, medication reviews are labour-intensive
and resources for this may be limited, given the projected global
shortage of healthcare workers forecast by the World Health Organization
(WHO) (32). Selection of high-risk patients would help make the process
more efficient and potentially reduce costs. Therefore, it is crucial to
use a sensitive and specific prediction tool to allocate these scarce
resources to patients in need of PE-mitigating interventions.
The systematic review of Deawjaroen et al. provides a comprehensive
overview of 14 currently available prediction tools and their clinical
usefulness in detecting adult, hospitalized patients at risk of
medication-related harm (18). Interestingly, the authors concluded that
none of the tools is optimal, possibly because of heterogeneity in
setting, outcome measures, content, and method of development and
validation of the prediction tools. Even though this might be a
plausible explanation, we would like to propose an alternative
hypothesis.
Prescribing medication in the in-hospital setting is a complex process,
influenced by multiple human and non-human factors (19, 29). All
available tools predicting medication-related harm combine multiple,
stand-alone risk factors, such as the number of prescribed drugs (8),
ageing or elderly age, impaired renal or hepatic function, and (33) the
use of specific high-risk medication, such as methotrexate and
non-steroidal anti-inflammatory drugs (NSAIDs) (9, 11-13), amongst
others (18). The Medicine Risk Score (MERIS) (17) and Drug-Associated
Risk Tool (DART) (33) are examples of prediction tools that use some of
these risk factors. Interestingly, all risk factors used in available
prediction tools are patient related, suggesting that medication-related
harm is predicted by patient-related factors only.
In a previous study, we made an overview of the factors, both protective
and facilitating, reported in the literature as influencing in-hospital
PEs (29). When these factors were classified in domains, it became clear
that domains other than patient-related factors also significantly
influenced in-hospital PEs. For example, organization, prescriber, and
technology-related factors. Yet none these potential risk factors are
included in currently available prediction tools, which might explain
the poor performance of these tools.
It therefore seems appropriate to include most, preferably all, factors
identified as influencing in-hospital PEs in future prediction tools
(29), that is, not only factors facilitating in-hospital PEs,
characteristic of a Safety-I approach, but also factors that protect
against in-hospital PEs, adopting a Safety-II perspective (34), which
better reflects daily practice and thus provides a realistic risk
estimation (35). While prediction tools should be easy and user-friendly
(28), sensitive, specific, and thus accurate, including all the factors
that influence in-hospital PEs might make the tool too cumbersome to
use. Artificial Intelligence (AI) is rapidly gaining a role in
healthcare delivery and may provide new possibilities in prediction tool
development. Nevertheless, developing any prediction tools, including
AI-driven, necessitates collecting, interpreting, and statistical
analyses of clinical data, which is a complex, labour-intensive, and
time-consuming undertaking. Therefore, there might be power in
simplicity.
Lynn et al. investigated the role of doctors on wards in identifying
patients who might benefit from palliative care services (36). To this
end, doctors were asked to identify patients based on reflecting on the
‘“surprise” question (SQ): Would it be surprising for this
patient to die in the next year? (or the next few months?)”, with the
answer “no’ being a trigger for referral to specialist services. This
stratification strategy has since been used in several patient
populations (37-39). To our knowledge, little is known about the basis
for doctors’ answers this question. Do they consider several factors,
such as quantifiable patient-related factors (e.g., frequent admissions
(40), as well as non- quantifiable measures? There are perhaps parallels
between the reasoning underlying the answer to the surprise question and
why ward doctors selected certain patients for a CMR. Factors taken into
consideration might include not only patient-related factors, such as a
patient’s habit of avoiding care, medication in adherence, questions
regarding the ability to keep an overview of medication regimens, ‘over
the counter’ medication use, and ordering medication over the internet,
but also whether a patient has a social safety network providing care,
whether adequate patient information was received when a patient was
transferred from another hospital, whether there was medication
reconciliation at hospital admission, if the ward was understaffed, or
if there were communication difficulties between the healthcare
professionals and the patient. Some doctors may have also selected
patients based on an ‘off feeling’ about a patient or wanted to check
their assessment. Interestingly, doctors appear to take their own
abilities into account in their risk prediction. For example, if they
felt that their (drug) knowledge, prescribing skills, or experience was
insufficient to form an opinion about a patient’s medication regimens,
they sought advice (29). Thus to identify patients at risk of
medication-related harm, doctors could perhaps ask themselves:
“Would I be surprised if there were prescribing errors in this
patient’s medication? ”. If the answer is ‘no’, then that patent’s
medication list should be reviewed. This simple prediction strategy uses
ward doctors as a predictive indicator, and is based on a doctor’s
clinical assessment, experience, and expectations. Therefore, this may
provide an easier, user-friendly, and accessible solution for the
prediction of patients at risk of in-hospital PEs.
Limitations & strengths
This study has both strengths and limitations. This is the first study
to suggest that ward doctors can identify patients at risk of PEs. This
prediction strategy does not require a priori data collection to
identify and statistically combine multiple predictors to estimate the
risk of PEs for an individual patient. Compared to currently available
prediction tools or AI, this strategy is easier to use in daily practice
(28). Unfortunately, due to the observational design of this study, only
patients suspected of PEs by the Otolaryngology and Oncology ward
doctors were assessed. This means that patients who were not suspected
of having PEs were not assessed by the ISP team. A future study should
include these patients in order to better determine the sensitivity and
specificity of this strategy.