Academic literature on the topic 'Prescription process'

Abstract To demonstrate a process of calculating the maximum potential morphine milligram equivalent daily dose (MEDD) based on the prescription Sig for use in quality improvement initiatives. To calculate an opioid prescription’s maximum potential Sig-MEDD, we developed SQL code to determine a prescription’s maximum units/day using discrete field data and text-parsing in the prescription instructions. We validated the derived units/day calculation using 3000 Sigs, then compared the Sig-MEDD calculation against the Epic-MEDD calculator. Of the 101 782 outpatient opioid prescriptions ordered over 1 year, 80% used discrete-field Sigs, 7% used free-text Sigs, and 3% used both types. We determined units/day and calculated a Sig-MEDD for 98.3% of all the prescriptions, 99.99% of discrete-Sig prescriptions, and 81.5% of free-text-Sig prescriptions. Analyzing opioid prescription Sigs to determine a maximum potential Sig-MEDD provides greater insight into a patient’s risk for opioid exposure.

199 Background: While team-based safety checks ensure safe prescribing of parenteral chemotherapy, oral chemotherapy is usually prescribed by a single clinician. With the growing use of oral chemotherapy, processes are needed to protect these vulnerable patients from prescription errors. Methods: A team of nurses, clinicians, pharmacists and administrators developed a new process and checklist for nursing verification of oral chemotherapy prescriptions at Dana-Farber’s pediatric neuro-oncology program. Prescriptions are verified against the treatment plan by two pediatric oncology nurses. The verification checklist includes drug, dose with any modifications, height and weight, laboratory values and patient instructions. When available, the prescription bottle is also verified. Data was collected over a three-month pilot period. Results: From 6/18/15-9/16/15, 56 prescription verifications occurred. Verification rate of on-site retail pharmacy filled prescriptions was 47% (32/68 prescriptions). Median time for verification was 20 minutes (IQR 15, 40) per nurse. Nurses identified problems outside of prescription verification, including missing prior authorizations and unclear treatment plans. Medication bottles were not routinely available for verification. One identified near miss would have resulted in an 80% under-dose of everolimus. Conclusions: Prescription verification by nursing in a pediatric oncology clinic was feasible. While it was successful in identification of one medication error before it reached the patient, only 47% of prescriptions were verified. Since prescription bottles are usually obtained after a visit, verification of the actual bottles will require new workflows, such as additional clinic visits or uploading a picture via the patient portal. Involving the nurse in the review of oral chemotherapy not only identified a prescription error, but also highlighted issues within other aspects of patients’ care, including inconsistent documentation of the treatment plan. The inclusion of nursing in the review and management of oral chemotherapy has the potential to improve safety and outcomes for these patients.

BackgroundWhilst the prescribing of both in-patient and discharge medicines is electronic, there was no automatic notification to clinical pharmacists when a discharge prescription was ready to be screened. The notification required a member of medical or nursing staff to bleep their pharmacist informing them of a prescription's availability. This manual process led to a delay in pharmacist screening which impacted on discharge. Prescriptions designated for pre-packed or patient's own medicine use were not seen at all by a clinical pharmacist. The initial intention was to develop a text messaging service; however this was not possible due to significant cost implications and its inflexibility.AimTo decrease the time to clinical pharmacist screening for children's discharge prescriptions.MethodA clinical pharmacist prescription alerting system was designed and implemented. The hospital's eDischarge Summaries are created and stored in the Trust's EPR database. A database query is executed that examines documents that have been signed by a prescriber which contain drug orders. The query runs every 15 minutes, Monday to Friday from 0800–2000. The database query exports a HTML data extract which is then packaged and sent using Exchange.Email was preferred as users access hospital WiFi, only receiving notifications on those laptops or smartphones connected to the Trust's email application. The HTML is embedded within the email body. The email is sent to named individuals within a given distribution list. The function is scalable to support all areas using Trust eDischarge Summaries.The system was introduced in April 2015. Data from before (June 2014–January 2015) and after (June 2015) implementation was compared.ResultsPrior to the introduction of an electronic alerting system the average time from a prescriber signing a prescription to clinical pharmacist screening was 93 minutes. Three months after starting the new system this time has reduced to 62 minutes, a reduction of 31 minutes or 33%. During the same time period, the number of discharge prescriptions screened by pharmacists rose from 172 to 218, an increase in workload of 26%.It has been possible to intervene on prescriptions containing errors which the clinical pharmacists would not previously have screened.ConclusionThe use of an electronic messaging system has met its primary aim to decrease the time delay from signing to pharmacist screening it has also increased pharmacist efficiency as evidenced by the increased workload.One limitation of this system is that it requires a regular e-mail check, for available prescriptions. The report runs every 15 minutes, an email is only sent if a prescription is found.The notification of all discharge prescriptions containing medicines has led to the identification of errors which have required intervention, in those prescriptions that a pharmacist would not have previously seen. These interventions have been for children who have received pre-packed antibiotics directly from the wards or for those where we have provided one-stop dispensing.It is hoped to role out this system across other areas of the organisation which should also enjoy this significant improvement in discharge prescription turnaround.

BackgroundLack of prescription adherence after discharge from the inpatient hospital setting is a barrier to the delivery of optimal patient care. Non-adherence to medication for cardiac diseases can lead to substantial morbidity, mortality and healthcare costs. Electronic delivery of prescriptions by fax is a potential method of improving patient satisfaction and reducing pharmacy wait times.MethodsThis study was completed in the cardiology inpatient wards at a hospital in London, Ontario, Canada. ‘Delayed prescription retrieval’ was defined as the retrieval of a prescribed medication by a patient from their local pharmacy after the documented calendar day of discharge. The current discharge process on the cardiology wards was assessed and an initial monitoring period of study participants was completed to determine the baseline delayed prescription retrieval rate (preintervention group). A formalised discharge process, which included electronic delivery of prescriptions to pharmacies by fax, was implemented for study participants (postintervention group). The rate of delayed prescription retrieval was assessed in both groups.Results15 of 42 patients (35.7%) in the preintervention group and 9 of 72 (14.3%) in the postintervention group had delayed prescription retrieval suggesting relative and absolute risk reductions of 65% and 23.2% (p=0.0045). Of the participants with delayed prescription retrieval, 100% in the preintervention group and 77.8% in the postintervention group were due a new prescribed medication on the day of discharge.ConclusionsPatients who experienced a formalised discharge process, which included electronic delivery of prescriptions by fax, at the time of discharge from cardiac inpatient care had a lower rate of delayed prescription retrieval. Future studies are required to examine the impact of formal discharge processes on patient morbidity and mortality.

Background:Medication error can be defined as a failure in the treatment process that leads to or has the potential to lead to harm to the patient, this fault can happen in two different phases: prescribing and prescription.Prescribing is the process of deciding what to prescribe and naming it. Various types of faults can occur in the decision-making process: underprescribing, overprescribing, irrational, inappropriate and ineffective prescribing. All these covers one type of errors, but these are different kind of errors that those that occur in the act of writing a prescription. This leads to the distinct concepts of ‘prescribing faults’ and ‘prescription errors’A prescription is ‘a written order, which includes detailed instructions of what medicine should be given, to whom, in what formulation and dose, by what route, when, how frequently, and for how long’. Thus, a prescription error can be defined as ‘a failure in the prescription writing process that results in a wrong instruction about one or more of the normal features of a prescription’. The ‘normal features’ include the identity of the patient, the identity of the drug, the formulation and dose, and the route, timing, frequency, and duration of administration. (1)It is not record about the rate of medication errors in rheumatology consultation.Objectives:To evaluate whether there is a relationship between prescribing errors and the number of drugs in the prescription.Methods:A descriptive, observational, and retrospective study was made.It was carried out a random search of medical prescriptions, generated by the electronic records (REPAIR®) of the rheumatology consultation of the Hospital Universitario “Dr. José Eleuterio González” during 2019, in which the prescriptions that contained any error were identifiedT student test was performed to see the difference in the prescription error based on the number of medications. P <0.05 was taken as statistically significant.Results:A review of 867 medical prescriptions was performed, among which 5503 medications were indicated with an average of 6.34 medications per prescription, a total of 30 (6.9%) prescriptions were found with error, where a total of 71 (3.9%) medications had errors. In the prescriptions with medication error, all the errors were prescription type; 68 (95.7%) had a mistake in the duration of administration and 3 (4.22%) in the identity of the drug.In the prescriptions with medical errors the average number of prescription drugs was 7.50, only 2/30 (0.6%) had less than 7 indicated medications (4 and 6), meanwhile the prescriptions in which no error was found had a mean of 6.30 indicated medications. P < 0.001.Conclusion:According to the study findings, it could be established that when the number of prescribed medications is greater than 7, there is an increased risk of making a prescription error. Further studies should carry out to look for other factors that influence medical errors in rheumatology clinics.References:[1]Aronson JK. Medication errors: definitions and classification. Br J Clin Pharmacol. 2009;67(6):599-604.AcknowledgmentsDisclosure of Interests:None declared

Purpose The purpose of this paper is to describe a case study undertaken at Al Buraimi Hospital in Oman, which used computer simulation and the Delphi approach to improve efficiency by reducing prescription dispensing waiting times. Design/methodology/approach This study’s framework was based on a discrete event simulation (DES) to identify the as-is pharmacy process and to create a to-be (future situation) to achieve an improvement in pharmacy workflow and service quality. Owing to healthcare environment complexity, and to gain a deeper understanding about Al Buraimi Hospital pharmacy problems, a Delphi technique was also used. Findings Based on Delphi, and according to the expert panel suggestions, two alternative scenarios were proposed to improve Al Buraimi Hospital pharmacy efficiency: fast-track and direct-dispensing, which should help to reduce the prescription dispensing waiting time process by 7.3 and 9.8 min, respectively. Research limitations/implications The main limitation is the pharmacists’ shortage, which may affect the prescription dispensing process’s quality as insufficient manpower to check the prescriptions may increase the medication errors’ risk. Originality/value Based on this case study’s real-world data, findings can be used to improve public healthcare sector pharmacy efficiency. The DES can be used in healthcare services to describe and test actual and proposed situations.

32 Background: The CMS’ Oncology Care Model (OCM) provides practices with enhanced monthly payments for beneficiaries with cancer receiving chemotherapy. While the program will distribute a retrospective beneficiary list, practices need to track and identify eligible patients upfront to initiate care management and financial counseling processes and to bill for the enhanced payment. The eligibility criteria require information that many practices do not have. We describe a stepwise approach to patient identification that can be used by other OCM practices. Methods: We ran a report that identified patients with Medicare who received an OCM-eligible drug. We classified drugs into four categories: IV chemo confirmed by our own billing, oral specialty tracked and/or filled by our specialty pharmacy, general prescriptions filled at offsite pharmacies, and oral drugs billed under Medicare Part B. Because patients on general oral drugs receive 11 refills when first prescribed, we could not rely on a new prescription to trigger enrollment; we created a candidate list of patients who received oral prescriptions in the last year. Our pharmacists manually checked QS1, to verify Medicare Part D status and Surescripts to confirm that patients filled their prescription. Results: The build took time to validate due to disparate data and incomplete insurance information. 1039 IV chemo, 249 oral chemo, 196 oral Medicare Part B, were definitively eligible. 2991 with general prescriptions made the oral candidate list, which required additional verification of last visit date, Medicare Part D status, and prescription fill date. Approximately 70% of our patients have Medicare Part D and over 90% filled their prescription. We billed IV for July and August and are awaiting final confirmation of drug fill before billing the patients from the oral categories. Conclusions: The patient identification process was more complex than expected. Implementation required a multi-disciplinary effort with extensive collaboration across several departments as well as a time-intensive manual insurance and drug fill-verification process. Opportunities exist to automate Medicare Part D verification using our real-time eligibility software.

Abstract Purpose To develop and implement an interprofessional framework to increase the capture of health system–generated prescriptions within health system–owned pharmacies. Summary Low prescription capture rates within a health system’s internal pharmacies led to an interdisciplinary process improvement effort. A framework was developed to assess the baseline prescription capture rate, select clinics for improvement, understand clinic workflows and key drivers of pharmacy selection, design strategies to increase prescription capture, implement targeted efforts, and measure the effectiveness of the intervention(s). Employing this framework provided revised workflows for nursing and medical assistant staff scripting and for referral of patients to internal pharmacies. These workflows were pilot tested at 3 system clinics. Results indicated that overall prescription capture increased by 2.9 to 4.1 percentage points (range, 10 to 86 prescriptions per month) and specialty prescription capture increased by 11.6 to 26.7 percentage points (range, 4 to 26 prescriptions per month) for each clinic within the first 2 months. A total of 99 new patients were referred to internal pharmacies within the first month. Conclusion Development and implementation of a framework to increase prescription capture from health system clinics helped increase capture, enhanced clinic engagement and knowledge about pharmacy services, and supported positive clinic-pharmacy relationships.

257 Background: At Dana-Farber Cancer Institute (DFCI), timing of order release to the pharmacy is a contributing factor to safety and processing concerns for oral investigational medications. Day-of release can lead to delayed delivery to the patient, creating a risk of missing timed specific protocol data collection, and rushed critical pharmacy safety checks, an issue raised in a comprehensive proactive systems safety risk assessment. We conducted a pilot project aimed at improving the safety and efficiency of oral investigational medication processing within the pharmacy by releasing prescription orders at least 24 hours in advance of a patient’s appointment. Methods: A team of pharmacists, nurses, process improvement professionals, and a physician designed a pilot project where the prescriber released oral investigational prescriptions, from 9 selected research protocols, at least 24 hours before a patient’s appointment. From 11/2/2018-3/1/2019, we used manual timestamp data to compare prescription processing times for prescriptions released at least 24 hours in advance (“released early”) to prescriptions released less than 24-hours in advance (“not released early”). Qualitative feedback was obtained to assess pilot impact on prescription processing safety. Results: As shown in the table below, prescription processing time on day of patient appointment for prescriptions released early was shorter, on average, compared to those not released early (p < 0.05). Due to orders being released early, pharmacy staff noted feeling less pressure during prescription checks and a better ability to proactively assess inventory and prescription issues. Conclusions: Releasing oral investigational prescriptions early reduced the prescription processing time and increased time available for safety checks. Expanding this workflow change to all investigational medication orders can increase the safety and efficiency of prescription processing at DFCI. [Table: see text]