The effects of pharmacist prescribing on patient outcomes in the hospital setting: a systematic review protocol
Bibliographic record
Abstract
Review question/objective The objective of this review is to determine the effects of pharmacist prescribing in the hospital setting. More specifically, the objectives are to quantitatively analyze the effectiveness of pharmacist prescribing on patient outcomes, including, but not limited to, the reduction of error rates and adverse events related to medication prescription in patients who present to hospital, either in the inpatient or outpatient setting. Background For conditions that can be medically managed, diagnosis is followed by prescribing of medications to treat a condition or alleviate symptoms associated with it. Traditionally, the act of prescribing has been mainly associated with medical practitioners. Non-medical prescribing is the extension of prescriptive rights provided to certain other professions apart from doctors, including nurses, pharmacists, optometrists and podiatrists. It was originally introduced to allow a more flexible system for the prescribing, supply and administration of medications in order to help improve patients' access to medications and ease the burden on general practitioners.1,2 Nurse prescribing was first introduced in the United States of America in 1969.3 In the last two decades, legislation changes have also occurred in various countries around the world to allow for non-medical prescribing.3-5 Pharmacist prescribing is currently legal in many countries, including Canada, New Zealand, the United Kingdom (UK) and the United States of America.2,6 In the UK, supplementary prescribing was first introduced in 2003, followed by independent prescribing in 2006.7 Different models of pharmacist prescribing have been described in the literature.1,5,6 They include independent, dependent and collaborative prescribing. In independent prescribing, pharmacists are responsible for the assessment, diagnosis and clinical management of patients. Dependent prescribing places more restrictions on the activity using protocols or formularies. The different types of dependent prescribing include: Prescribing by protocol: a written guideline (protocol) describes in explicit detail the activities that may be performed by the non-medical practitioner. This protocol includes the types of diseases and drug classes in which the practitioner may prescribe. Patient group directions: a written direction relating only to supply and administration of a prescription medicine. Prescribing by formulary: a limited list of medicines with limitations on prescribing. Prescribing by patient referral: patients are referred by a physician for specific drug therapy management or to achieve a defined therapeutic outcome. Repeat prescribing: medication-refill services in clinics for patients who require new continuing prescriptions prior to their next available appointment with their physician. Supplementary prescribing: a voluntary partnership between the physician and pharmacist, where the physician undertakes the initial assessment and the supplementary prescriber (pharmacist) prescribes in accordance with the care plan which has been agreed by the physician and patient. In collaborative prescribing, there is a cooperative practice relationship between the pharmacist and physician, where the pharmacist may prescribe medications. The physician diagnoses and makes initial treatment decisions for the patient while the pharmacist selects, monitors, modifies, continues or discontinues the treatment as appropriate. While systematic reviews on nurse prescribing are available,8,9 there are currently no systematic reviews available to quantify the effects of pharmacist prescribing in the hospital setting. One review published in 2011 assessed the contribution of prescribing by nurses and allied health professionals, but this was limited to the primary care setting.3 In 2004, a review focusing on pharmacist prescribing was published, and included prescribing in both the community and hospital setting.10 This review identified only four studies with an experimental design and concluded that additional research was needed to establish the validity of pharmacist prescribing. In a review which evaluated the impact of pharmacists on mental health, some studies involving pharmacist prescribing were included but were not the main focus of the review.11 Other published reviews which have included pharmacist prescribing mainly relate to descriptions of its current practice (including existing policies and procedures) in a specific country or region, barriers to its successful implementation, or the perspectives of pharmacist prescribers, other healthcare professionals or patients on pharmacist prescribing.2,6,12 A systematic review on the effects of pharmacist prescribing on patient outcomes in the hospital setting is therefore warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".