Combining the medication reconciliation and review process at initial hospital admission (Admission part) with a quality-controlled medication report at discharge from hospital (Discharge part) was shown to generate both cost savings and higher utility to the patients. In fact, investing €39 in clinical pharmacist time could save €340 in medical care at hospital and in primary care, as well as in administrative costs for correcting errors in medication lists in primary and municipality care after discharge. Furthermore, the analysis showed that the potential for cost off-set was greatest for systematic medication reconciliation and review process at initial hospital admission due to avoided costly hospitalisations.
The main data sources supporting the probabilities for unplanned rehospitalisations, outpatient visits and prescription errors were based on two studies.6
One of the studies was a controlled pre–post study and the outcome was assessed blind.6
In a recent systematic review of hospital-based medication reconciliation practice, this study was evaluated as a non-controlled pre–post study and was consequently erroneous evaluated as being of poor quality.35
However, it must be stated that some of the reported resource utilisation (costs) did not show statistically significant differences between the treatment arms for example, the hospitalisation cost. Small patient samples and great variability in the studied variable is often the reason for this. All bias and confounders could not be ruled out since the studies were not randomised and controlled for. However, in our opinion patient-based randomisation could be problematic in team-based interventions. There is of course a risk of bias due to carry over effects decreasing the difference between groups. But there is also a risk of bias increasing the difference between groups owing to a decreased level of care in the control group than before introducing the intervention. There is a need for higher attention and further studies in this field.
The time utilisation in primary and municipality care for calculating administrative costs for error corrections was also based on surveys.27
In addition, utilities were taken from the literature and may not entirely reflect the analysed patient population. This are, of course, weaknesses in this study, but nevertheless, the resulting cost savings were shown to be stable in spite of several sensitivity analyses which indicated that these shortcomings may be of less importance. Hence, discarding the assumed utilities, the model would still be valid for a cost-minimisation analysis.
Apart from just estimating the costs and effects from the two main studies, we also modelled the consequences of quality-controlled medication lists. The effects from this control may have been conservatively estimated as the reduction in errors was assumed to only reduce the time devoted to correct these errors. One could argue that some of these avoided errors could have had an impact also on the probabilities of unplanned rehospitalisations and outpatient visits. However, the potential gain from these unplanned healthcare contacts in the Discharge part would be limited to less than €17 in the intervention arm. However, the benefit from the LIMM-discharge part has probably been improved since the initial study performed in 2005.21
The medication report is now part of the LIMM-discharge information and this have been shown to improve time-utilisation for general practitioners and community care nurses.26
As previously described, several studies present positive economic benefits from clinical pharmacy services study.15
Bojke et al
performed a health economic analysis on the RESPECT trial (Randomised Evaluation of Shared Prescribing for Elderly people in the Community over Time) which included services similar to our study.15
The RESPCT trial measured both resource use and utility of the patients but did not attain statistically significant differences in outcomes. Their intervention was expected to cost an extra £192 per patient and year with a gain of 0.019 QALY, resulting in an incremental cost-utility ratio of approximately £10 000 (2004–2005 prices). Apart from differences in healthcare structures between the UK and Sweden, the discrepancy in results may be attributable to the fact that the RESPECT trial was a primary care-based pharmaceutical care intervention whereas the LIMM process was hospital based.
A recent Swedish study providing similar services as in LIMM concluded that a hospital-based clinical pharmacist was not cost effective according to the Swedish willingness to pay for a gained QALY.16
In fact, the cost in the intervention arm tended to be higher than in the control arm, and with only marginal QALY-gains. The authors discuss some potential reasons for this outcome, eg, the use of inexperienced pharmacists. In the LIMM model, the pharmacists were fully integrated in the care team and worked very structured and systematic. The difference between the studies’ results could therefore describe a learning curve and or the benefit of a trustful care team supporting the patient. Furthermore, our cost analysis included only hospitalisations that were considered drug-related during a 3-month period after discharge whereas Wallerstedt et al
included all hospitalisations during 6 months. When including probabilities only for drug-related hospitalisations, we avoided hospitalisations due to differences in patient characteristics and comorbidities between the study arms. Still, historical controls’ medical records were scrutinised to identify ‘certain’, ‘probable’ or ‘possible’ hospital readmissions, which may introduce bias from either too strict or too loose rules for causality.
Hence, the size of the gains may not be permanent. In addition, one could argue that the cost effectiveness may be reduced as more and more medication lists will eventually have been reviewed. However, the errors analysed in the LIMM model are often generated during the hospital stay why we believe this process is important to improve the care given and to save resources even in the future. Furthermore, the elderly part of the Swedish population will increase and, hence, the disease burden. Thus, the scope for cost-savings may change with the development of the healthcare structure and internal organisations, why further research is warranted.
The results from this study can be used for allocating resources where the expected outcome is the most favourable. However, it is important that the gains may not be limited to financial resources and utility for the patients. Some physical resources may be in scarcity, such as physicians or nurses, why it may be important to also consider potential bottlenecks in the healthcare process. If, for example, there are a limited number of hospital beds at a ward, it may be recommended to invest in a process reducing the hospitalisations due to medication errors. This could free resources to other patients and probably reduce the distress of the personnel at the ward. In the same way, the time devoted by nurses at nursing homes and physicians reviewing medication lists after hospital discharge can be spent on other tasks.
Rescaling our results to a situation where we have approximately 150 000 hospital admissions in the Southern healthcare region with similar patient characteristics as modelled here, this would mean that some €51 million could be saved per year if the LIMM process was rolled out in the entire region. As it is today, the physician is already, by law, supposed to provide medication discharge information, but this is poorly complied with.15
Maybe a pay-for-performance could provide a good incentive to get the physicians to provide a quality-controlled medication report at discharge.