OBJECTIVE To evaluate the effects of a clinical pharmacist service on health-related quality of life (HRQL) and prescribing of drugs. METHODS A randomised controlled study was performed in two internal medicine wards. The intervention consisted of medication reviews with feedback to the physicians, drug treatment discussion with patients at discharge and medication reports. HRQL was evaluated at inclusion and after six months by self-rated global health (1: very poor; 5: very good) and by the EuroQol 5-dimension questionnaire (EQ-5D). Prescribing of drugs was analysed regarding three established drug-specific quality indicators (intervention and control patients) and potential drug-related problems (DRPs) during in-hospital care (intervention patients). RESULTS 345 patients (61% female; median age: 82) were analysed, 204 of whom (59%) completed the six-month HRQL follow-up. A total of 87 patients (53% of the intervention patients) received all parts of the intervention. Intention-to-treat analysis revealed no significant findings for any of the HRQL measures. Per-protocol analysis revealed significantly better HRQL in the intervention group at six-month follow-up as measured by global health (mean: 3.14 (SD: 0.87) vs 2.77 (0.94), p=0.020), but not as measured by summarised EQ-5D index (0.48 (0.36) vs 0.43 (0.37), p=0.57). The number of potentially inappropriate prescribings per patient according to the quality indicators (admission vs discharge) was 0.35 (0.73) versus 0.38 (0.72), p=0.47 (control patients), and 0.39 (0.83) versus 0.26 (0.56), p=0.039 (intervention patients who received the intervention). In the intervention group, 133 relevant potential DRPs were identified in 81 patients, 55 of which (41%) were acted upon by the attending physician. CONCLUSION A clinical pharmacist service during inpatient care may improve quality of prescribing and patients' HRQL. Trial registration clinicaltrials.gov Identifier: NCT01016301.

Treatment with fall-risk-increasing drugs was extensive among older hip fracture patients both before and after the fracture. The proportion of patients with fracture-preventing drugs was low at admission and increased slightly during the follow-up period. Hence, drug treatment in older hip fracture patients can be improved regarding both fall-risk-increasing drugs and fracture-preventing drugs.

Setting: Two internal medicine wards at Sahlgrenska University Hospital, Göteborg, Sweden.Participants: Of 345 patients (61% women; median age: 82 years; 181 control and 164 intervention patients), 240 patients (62% women, 82 years; 124 control and 116 intervention patients) had EuroQol-5 dimensions (EQ-5D) utility scores at baseline and at 6-month follow-up.Outcome measures: Costs during a 6-month follow-up period in all patients and incremental cost-effectiveness ratio per quality-adjusted life-year (QALY) in patients with EQ-5D utility scores. Inpatient and outpatient care was extracted from the VEGA database. Drug costs were extracted from the Swedish Prescribed Drug Register. A probabilistic analysis was performed to characterise uncertainty in the costeffectiveness model. Results:No significant difference in costs between the randomisation groups was found; the mean total costs per individual6SD, intervention costs included, were V10 748613 799 (intervention patients) and V10 344614 728 (control patients) (p¼0.79). For patients in the cost-effectiveness analysis, the corresponding costs were V10 912613 999 and V9290612 885. Intervention patients gained an additional 0.0051 QALYs (unadjusted) and 0.0035 QALYs (adjusted for baseline EQ-5D utility score). These figures result in an incremental costeffectiveness ratio of V316 243 per unadjusted QALY and V463 371 per adjusted QALY. The probabilistic uncertainty analysis revealed that, at a willingness-to-pay of V50 000/QALY, the probability that the intervention was cost-effective was approximately 0.2.Conclusions: The present study reveals that an intervention designed like this one is probably not cost-effective. The study thus illustrates that the complexity of healthcare requires thorough health economics evaluations rather than simplistic interpretation of data.