Measurement of selected preventable adverse drug events in electronic health records: Toward developing a complexity score

Care complexity individual factors associated with adverse events and in-hospital mortality

Introduction

Measuring the impact of care complexity on health outcomes, based on psychosocial, biological and environmental circumstances, is important in order to detect predictors of early deterioration of inpatients. We aimed to identify care complexity individual factors associated with selected adverse events and in-hospital mortality.

Methods

A multicenter, case-control study was carried out at eight public hospitals in Catalonia, Spain, from January 1, 2016 to December 31, 2017. All adult patients admitted to a ward or a step-down unit were evaluated. Patients were divided into the following groups based on the presence or absence of three adverse events (pressure ulcers, falls or aspiration pneumonia) and in-hospital mortality. The 28 care complexity individual factors were classified in five domains (developmental, mental-cognitive, psycho-emotional, sociocultural and comorbidity/complications). Adverse events and complexity factors were retrospectively reviewed by consulting patients’ electronic health records. Multivariate logistic analysis was performed to identify factors associated with an adverse event and in-hospital mortality.

Results

A total of 183,677 adult admissions were studied. Of these, 3,973 (2.2%) patients experienced an adverse event during hospitalization (1,673 [0.9%] pressure ulcers; 1,217 [0.7%] falls and 1,236 [0.7%] aspiration pneumonia). In-hospital mortality was recorded in 3,996 patients (2.2%). After adjustment for potential confounders, the risk factors independently associated with both adverse events and in-hospital mortality were: mental status impairments, impaired adaptation, lack of caregiver support, old age, major chronic disease, hemodynamic instability, communication disorders, urinary or fecal incontinence, vascular fragility, extreme weight, uncontrolled pain, male sex, length of stay and admission to a medical ward. High-tech hospital admission was associated with an increased risk of adverse events and a reduced risk of in-hospital mortality. The area under the ROC curve for both outcomes was > 0.75 (95% IC: 0.78–0.83).

Conclusions

Several care complexity individual factors were associated with adverse events and in-hospital mortality. Prior identification of complexity factors may have an important effect on the early detection of acute deterioration and on the prevention of poor outcomes.

Predicting medication-associated altered mental status in hospitalized patients: Development and validation of a risk model

PURPOSE

This study presents a medication-associated altered mental status (AMS) risk model for real-time implementation in inpatient electronic health record (EHR) systems.

METHODS

We utilized a retrospective cohort of patients admitted to 2 large hospitals between January 2012 and October 2013. The study population included admitted patients aged ≥18 years with exposure to an AMS risk-inducing medication within the first 5 hospitalization days. AMS events were identified by a measurable mental status change documented in the EHR in conjunction with the administration of an atypical antipsychotic or haloperidol. AMS risk factors and AMS risk-inducing medications were identified from the literature, drug information databases, and expert opinion. We used multivariate logistic regression with a full and backward eliminated set of risk factors to predict AMS. The final model was validated with 100 bootstrap samples.

RESULTS

During 194,156 at-risk days for 66,875 admissions, 262 medication-associated AMS events occurred (an event rate of 0.13%). The strongest predictors included a history of AMS (odds ratio [OR], 9.55; 95% confidence interval [CI], 5.64-16.17), alcohol withdrawal (OR, 3.34; 95% CI, 2.18-5.13), history of delirium or psychosis (OR, 3.25; 95% CI, 2.39-4.40), presence in the intensive care unit (OR, 2.53; 95% CI, 1.89-3.39), and hypernatremia (OR, 2.40; 95% CI, 1.61-3.56). With a C statistic of 0.85, among patients scoring in the 90th percentile, our model captured 159 AMS events (60.7%).

CONCLUSION

The risk model was demonstrated to have good predictive ability, with all risk factors operationalized from discrete EHR fields. The real-time identification of higher-risk patients would allow pharmacists to prioritize surveillance, thus allowing early management of precipitating factors.

Development of a predictive model for drug-associated QT prolongation in the inpatient setting using electronic health record data

PURPOSE

We aimed to construct a dynamic model for predicting severe QT interval prolongation in hospitalized patients using inpatient electronic health record (EHR) data.

METHODS

A retrospective cohort consisting of all adults admitted to 2 large hospitals from January 2012 through October 2013 was established. Thirty-five risk factors for severe QT prolongation (defined as a Bazett's formula-corrected QT interval [QTc] of ≥500 msec or a QTc increase of ≥60 msec from baseline) were operationalized for automated EHR retrieval; upon univariate analyses, 26 factors were retained in models for predicting the 24-hour risk of QT events on hospital day 1 (the Day 1 model) and on hospital days 2-5 (the Days 2-5 model).

RESULTS

A total of 1,672 QT prolongation events occurred over 165,847 days of risk exposure during the study period. C statistics were 0.828 for the Day 1 model and 0.813 for the Days 2-5 model. Patients in the upper 50th percentile of calculated risk scores experienced 755 of 799 QT events (94%) allocated in the Day 1 model and 804 of 873 QT events (92%) allocated in the Days 2-5 model. Among patients in the 90th percentile, the Day 1 and Days 2-5 models captured 351 of 799 (44%) and 362 of 873 (41%) QT events, respectively.

CONCLUSION

The risk models derived from EHR data for all admitted patients had good predictive validity. All risk factors were operationalized from discrete EHR fields to allow full automation for real-time identification of high-risk patients. Further research to test the models in other health systems and evaluate their effectiveness on outcomes and patient care in clinical practice is recommended.

ASHP Practice Advancement Initiative 2030: New recommendations for advancing pharmacy practice in health systems

Purpose

The process of updating the recommendations of the ASHP Practice Advancement Initiative (PAI) is described, and the new recommendations targeted toward the year 2030 are presented.

Summary

The process for updating ASHP recommendations for pharmacy-practice change included online surveys of pharmacists, pharmacy technicians, and other stakeholders; extensive discussions by an advisory panel, a strategic planning group, and participants in a town hall session at a national conference; an online public comment period; and final approval by the ASHP Board of Directors.

Conclusion

The guidance offered by the 59 updated PAI recommendations, which take into account environment trends that are likely to affect the pharmacy enterprise, will help health-system pharmacists in their ongoing pursuit of optimal, safe, and effective use of medicines.

Implementation and use of an electronic health record in a charitable community pharmacy

OBJECTIVES

To describe the implementation of enhanced health information technology (HIT), specifically an electronic health record (EHR), into the workflow of a charitable community pharmacy and to highlight the impact of the EHR on clinical service advancement, student and resident learning, research, and grant support for the pharmacy.

SETTING

The Charitable Pharmacy of Central Ohio (CPCO) is a nonprofit community pharmacy that provides medications and pharmacy services for uninsured and underinsured patients.

PRACTICE DESCRIPTION

CPCO has adopted a practice model in which patients discuss their medications and health conditions in a private counseling area with a pharmacist or pharmacy student. Counseling sessions incorporate point-of-care testing, medication therapy management, and community program referrals, with documentation of the visit in the patient's chart.

PRACTICE INNOVATION

This article describes the implementation of a cloud-based EHR in a charitable community pharmacy.

EVALUATION

The decision-making process for converting from a paper-based chart to an EHR is described. Feedback from stakeholders, discussions at staff meetings, and a quality improvement project led by 2 pharmacy residents helped to inform and improve the process.

RESULTS

Implementation of an EHR has allowed CPCO to improve documentation of patient encounters and communicate more effectively and efficiently with other health care professionals. Student and resident learning has been enhanced, and reporting tools have facilitated additional opportunities for successful funding and more robust research.

CONCLUSION

The use of an EHR at CPCO has provided opportunities to enhance patient care and improve other areas of practice. Community pharmacies should consider the utilization of HIT and EHRs to demonstrate the impact on patient care, elevate the standard of practice, and offer support for provider status.