Objectives To identify factors that differentiate between effective and ineffective computerised clinical decision support systems in terms of improvements in the process of care or in patient outcomes.Design Meta-regression analysis of randomised controlled trials.Data sources A database of features and effects of these support systems derived from 162 randomised controlled trials identified in a recent systematic review. Trialists were contacted to confirm the accuracy of data and to help prioritise features for testing.Main outcome measures "Effective" systems were defined as those systems that improved primary (or 50% of secondary) reported outcomes of process of care or patient health. Simple and multiple logistic regression models were used to test characteristics for association with system effectiveness with several sensitivity analyses.Results Systems that presented advice in electronic charting or order entry system interfaces were less likely to be effective (odds ratio 0.37, 95% confidence interval 0.17 to 0.80). Systems more likely to succeed provided advice for patients in addition to practitioners (2.77, 1.07 to 7.17), required practitioners to supply a reason for over-riding advice (11.23, 1.98 to 63.72), or were evaluated by their developers (4.35, 1.66 to 11.44). These findings were robust across different statistical methods, in internal validation, and after adjustment for other potentially important factors. ConclusionsWe identified several factors that could partially explain why some systems succeed and others fail. Presenting decision support within electronic charting or order entry systems are associated with failure compared with other ways of delivering advice. Odds of success were greater for systems that required practitioners to provide reasons when over-riding advice than for systems that did not. Odds of success were also better for systems that provided advice concurrently to patients and practitioners. Finally, most systems were evaluated by their own developers and such evaluations were more likely to show benefit than those conducted by a third party.

Objectives To describe the prevalence of unplanned hospitalizations caused by ADRs among older Veterans and examine the association between this outcome and polypharmacy after controlling for comorbidities and other patient characteristics. Design Retrospective cohort. Setting Veterans Affairs Medical Centers (VAMC). Participants 678 randomly selected unplanned hospitalizations of older (age ≥ 65 years) Veterans between 10/01/03 and 09/30/06. Measurements Naranjo ADR algorithm, ADR preventability, and polypharmacy (0–4, 5–8, and ≥ 9 scheduled medications). Results Seventy ADRs involving 113 drugs were determined in 68 (10%) older Veterans’ hospitalizations, of which 36.8% (25/68) were preventable. Extrapolating to the population of over 2.4 million older Veterans receiving care during the study period, 8,000 hospitalizations may have been unnecessary. The most common ADRs that occurred were bradycardia (n=6; beta blockers, digoxin), hypoglycemia (n=6; sulfonylureas, insulin), falls (n=6; antidepressants, ACE-inhibitors), and mental status changes (n=6; anticonvulsants, benzodiazepines). Overall, 44.8% of Veterans took ≥ 9 outpatient medications and 35.4% took 5–8. Using multivariable logistic regression and controlling for demographic, health status, and access to care variables, polypharmacy (≥ 9 and 5–8) was associated with an increased risk of ADR-related hospitalization (AOR 3.90, 95% CI 1.43–10.61 and AOR 2.85, 95% CI 1.03–7.85, respectively). Conclusion ADRs determined by a validated causality algorithm are a common cause of unplanned hospitalization among older Veterans, are frequently preventable, and are associated with polypharmacy.

Thousands of Americans are injured or die each year from adverse drug reactions, many of which are preventable. The burden of harm conveyed by the use of medications is a significant public health problem and, therefore, improving the medication-use process is a priority. Recent and ongoing efforts to improve the medication-use process focus primarily on improving medication prescribing, and not much emphasis has been put on improving medication discontinuation. A formalized approach for rationally discontinuing medications is a necessary antecedent to improving medication safety and improving the nation's quality of care. This paper proposes a conceptual framework for revising the prescribing stage of the medication-use process to include

Background Risk for acute kidney injury (AKI) in older adults has not been systematically evaluated. We sought to delineate the determinants of risk for AKI in older compared to younger adults. Study Design Retrospective analysis of patients hospitalized in July 2000–September 2008. Setting & Participants We identified all adult patients admitted to an intensive care unit (ICU) (n=45,655) in a large tertiary care university hospital system. We excluded patients receiving dialysis or kidney transplant prior to hospital admission, and patients with baseline creatinine ≥ 4 mg/dl, liver transplantation, indeterminate AKI status, or unknown age, leaving 39,938 patients. Predictor We collected data on multiple susceptibilities and exposures including age, sex, race, body mass, comorbid conditions, severity of illness, baseline kidney function, sepsis, and shock. Outcomes We defined AKI according to KDIGO (Kidney Disease: Improving Global Outcomes) criteria. We examined susceptibilities and exposures across age strata for impact on development of AKI. Measurements We calculated area under the receiver operating characteristic curve (AUC) for prediction of AKI across age groups. Results 25,230 patients (63.2%) were aged 55 years or older. Overall 25,120 patients (62.9%) developed AKI (69.2% aged 55 years or older). Examples of risk factors for AKI in the oldest age category (75 years or older) were drugs (vancomycin, aminoglycosides, nonsteroidal anti-inflammatories), history of hypertension (OR, 1.13; 95% CI, 1.02–1.25) and sepsis (OR, 2.12; 95% CI, 1.68–2.67). Fewer variables remained predictive of AKI as age increased and the model for older patients was less predictive (p<0.001). For the age categories 18–54, 55–64, 65–74, and 75 years or older, the AUCs were 0.744 (95% CI, 0.735–0.752), 0.714 (95% CI, 0.702–0.726), 0.706 (95% CI, 0.693–0.718), and 0.673 (95% CI, 0.661–0.685), respectively. Limitations Analysis may not apply to non-ICU patients. Conclusions The likelihood of developing AKI increases with age; however the same variables are less predictive for AKI as age increases. Efforts to quantify risk for AKI may be more difficult in older adults.