[Evaluation of the collaboration between Primary and Hospital Care in order to improve inappropriate prescription]

[Interlevel incident management: The experience of a primary care district]

AIM

Determine the prevalence and define the profile of interlevel incidences (ININ) between primary care (PC) and hospital (HC).

DESIGN

Multicenter cross-sectional descriptive study. SITE: Primary care.

PARTICIPANTS

Professionals from a Health District and its reference hospitals.

INTERVENTIONS

ININ are errors in communication between PC and HC professionals derived from administrative, pharmaceutical or clinical procedures not resolved during the formal interlevel communication processes, which requires a coordinated and validated response from the health care directions to not overload the family physician.

MAIN MEASUREMENTS

ININ by category, hospital services and health centers, total and validated, relative to the total number of referrals, and the reason for the ININ.

RESULTS

We detected 2011 ININs (3.36%) among the 59.859 referrals, although only 1684 were validated (83.7%). Most were administrative (59.5%), followed by pharmaceutical (24.2%), clinical (10.2%) and reverse (6.1%). 41.3% of the clinical ININs were grouped around 5 hospital specialties, and 45.9% in 5 health centers. The main reasons for clinical ININ were non-prescription of the recommended pharmacological treatment in outpatient clinics or on hospital discharge (27.3%), request for referral to another hospital specialist (27.9%), or request to referral in person to patients who had already been referred by teleconsultation (17.8%).

CONCLUSIONS

3.36% of interlevel referrals are accompanied by incidents and 83.7% are validated and processed. It is necessary to develop ININ management tools to guarantee safe healthcare and debureaucratize PC.

Feasibility of the Implementation of LESS-CHRON in Clinical Practice: A Pilot Intervention Study in Older Patients With Multimorbidity

Background and Objectives

Potentially inappropriate medication refers to the prescription of drugs whose risks outweigh the benefits. There are different pharmacotherapeutic optimization strategies to detect and avoid potentially inappropriate medications (PIMs), namely deprescription. The List of Evidence-Based Deprescribing for Chronic Patients (LESS-CHRON) criteria were designed as a tool to systematize the deprescribing process. LESS-CHRON has established itself as one of the most suitable to be applied in older (≥65 years) multimorbid patients. However, it has not been applied to these patients, to measure the impact on their treatment. For this reason, a pilot study was conducted to analyze the feasibility of implementing this tool in a care pathway.

Research Design and Methods

A pre-post quasi-experimental study was conducted. Older outpatients with multimorbidity from the Internal Medicine Unit of a benchmark Hospital were included. The main variable was feasibility in clinical practice, understood as the likelihood that the deprescribing intervention recommended by the pharmacist would be applied to the patient. Success rate, therapeutic, and anticholinergic burden, and other variables related to health care utilization were analyzed.

Results

A total of 95 deprescribing reports were prepared. Forty-three were evaluated by the physician who assessed the recommendations made by pharmacists. This translates into an implementation feasibility of 45.3%. The application of LESS-CHRON identified 92 PIMs. The acceptance rate was 76.7% and after 3 months 82.7% of the stopped drugs remained deprescribed. A reduction in anticholinergic burden and enhanced adherence was achieved. However, no improvement was found in clinical or health care utilization variables.

Discussion and Implications

The implementation of the tool in a care pathway is feasible. The intervention has achieved great acceptance and deprescribing has been successful in a not insignificant percentage. Future studies with a larger sample size are necessary to obtain more robust results in clinical and health care utilization variables.