scholarly journals Assessing DNA rates within first time psychiatric referrals and the extent to which DNA rates are reduced by an SMS reminder service

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S315-S315
Author(s):  
Henry Coates
Keyword(s):  
Data Collection ◽  
Waiting Time ◽  
Text Messaging ◽  
Positive Impact ◽  
Waiting Times ◽  
National Guidelines ◽  
Medical Specialties ◽  
Number Of Patients ◽  
First Time ◽  
Communication Preference

Aims1) To assess the average wait time for patients to be offered an appointment and to establish any correlations between longer waiting times and 'Did not attend (DNA)' rates 2) To assess the number of patients who have opted into the text message appointment reminder service and whether this had an effect on DNA rates.BackgroundResearch has indicated that the Did Not Attend (DNA) rate in Psychiatry is estimated at 20%, twice that of other medical specialties (1). With NHS Digital estimating that DNAs cost the NHS £1 Billion per annum, there has been much interest in reducing the rate of DNAs within Psychiatry (2). Findings have shown that short waiting times are associated with higher rates of attendance (3). In addition, poor appointment attendance within Psychiatry is also associated with increased disease severity and higher rates of hospital admission (4).MethodWe conducted retrospective data collection on 99 patients referred to Professor Oyebode between January 2018 and August 2019. Our data collection involved assessing time the referral was received, time to first appointment and the patient's communication preference (e.g. whether they opted in to the SMS alert service). All data collection was conducted through use of RIO and coded/ammonized into a Excel spreadsheet. No sampling methods were employed and our population only consisted of first-time referrals to Professor Oyebodes clinic.Result1) We found no correlation between a longer waiting time to first appointment and an increased DNA rate.2) All patient waiting times between 1st January - 31st August were within the maximum limit set by national guidelines3) Opting into the text messaging service remains severely low. Of the patients audited, 95% had not completed a communication preference form. Overall, it is still unclear whether the text messaging service has a positive impact on DNA rates.ConclusionOur data have shown no significant correlation between a longer waiting time and an increased DNA rate for first time Psychiatry appointments. Secondly, we have concluded that between the audited period, waiting times were still within the maximum 18 week wait set by the Mental Health Standards. Finally, we can conclude that uptake of the text messaging service remains very low at 4%. Due to a limited sample size of only 4 patients, it is still unclear from this audit whether opting into the text messaging services will have a positive decrease on the number of DNA's.

HOW TO REPORT AND MONITOR THE PERFORMANCE OF WAITING LIST MANAGEMENT

2002 ◽  
Vol 18 (3) ◽  
pp. 611-618
Author(s):  
Markus Torkki ◽  
Miika Linna ◽  
Seppo Seitsalo ◽  
Pekka Paavolainen
Keyword(s):  
Hallux Valgus ◽  
Waiting Time ◽  
Varicose Vein ◽  
Elective Surgery ◽  
Waiting Times ◽  
Waiting List ◽  
Mean Waiting Time ◽  
Number Of Patients ◽  
Potential Problems ◽  
Queue Discipline

Objectives: Potential problems concerning waiting list management are often monitored using mean waiting times based on empirical samples. However, the appropriateness of mean waiting time as an indicator of access can be questioned if a waiting list is not managed well, e.g., if the queue discipline is violated. This study was performed to find out about the queue discipline in waiting lists for elective surgery to reveal potential discrepancies in waiting list management. Methods: There were 1,774 waiting list patients for hallux valgus or varicose vein surgery or sterilization. The waiting time distributions of patients receiving surgery and of patients still waiting for an operation are presented in column charts. The charts are compared with two model charts. One model chart presents a high queue discipline (first in—first out) and another a poor queue discipline (random) queue. Results: There were significant differences in waiting list management across hospitals and patient categories. Examples of a poor queue discipline were found in queues for hallux valgus and varicose vein operations. Conclusions: A routine waiting list reporting should be used to guarantee the quality of waiting list management and to pinpoint potential problems in access. It is important to monitor not only the number of patients in the waiting list but also the queue discipline and the balance between demand and supply of surgical services. The purpose for this type of reporting is to ensure that the priority setting made at health policy level also works in practise.

scholarly journals OPTIMASI SISTEM ANTRIAN PADA LAYANAN KESEHATAN MASYARAKAT MENGGUNAKAN PENDEKATAN SIMULASI

2019 ◽  
Vol 3 (1) ◽  
pp. 14-22
Author(s):  
Widya Setia Findari ◽  
Yohanes Anton Nugroho
Keyword(s):  
Waiting Time ◽  
Six Sigma ◽  
Waiting Times ◽  
Community Health Center ◽  
Observation Data ◽  
Simulation Approach ◽  
Simulation Test ◽  
Queue System ◽  
Number Of Patients ◽  
Patient Service

Abstract : The purpose of this study is to optimize service time in a community health center. The average number of patients visiting is 100 to 300 per day. In certain units there is a heavy queue of patients which increases service waiting times, including registration units, inspection units, and pharmaceutical units. The initial observation data on the existing system shows the waiting time for patient services is 2,7 hours. This fact shows that the time of patient service on the existing system needs to be optimized so that the waiting time can be accelerated. This study offers a solution to optimize the service queue system using a simulation approach. The DMAIC (Define, Measure, Analyze, Improve, Control) Six Sigma method is used as a basis for analyzing the waiting time for services from an existing system. The results of the analysis are used in the simulation test to obtain improvement factors using several scenarios. The best simulation results are obtained with the scenario of adding operators in all units. Optimizing the waiting time of patient services using the best scenario simulation approach is shown by the decrease in waiting time of the queue system by 1,05 hours or 38,9% faster than the existing system.Keywords: System Optimizing; Public Health; Queue; Simulation; DMAIC Six SigmaAbstrak : Tujuan penelitian ini adalah untuk mengoptimalkan waktu tunggu pelayanan di sebuah pusat kesehatan masyarakat (Puskesmas). Rata-rata jumlah pasien yang berkunjung adalah 100 hingga 300 per hari. Pada beberapa unit tertentu terjadi antrian pasien yang padat sehingga meningkatkan waktu tunggu pelayanan, antara lain unit pendaftaran, unit pemeriksaan, dan unit farmasi. Data pengamatan awal pada sistem yang ada menunjukkan waktu tunggu pelayanan pasien adalah 2,7 jam. Fakta ini menunjukkan bahwa waktu pelayanan pasien pada sistem yang ada perlu dioptimalkan agar waktu tunggu dapat dipercepat. Penelitian ini menawarkan solusi optimalisasi sistem antrian pelayanan dengan menggunakan pendekatan simulasi. Metode DMAIC (Define, Measure, Analyze, Improve, Control) Six Sigma digunakan sebagai dasar analisis waktu tunggu pelayanan dari sistem yang sudah ada. Hasil analisis digunakan pada uji simulasi untuk mendapatkan faktor perbaikan dengan menggunakan beberapa skenario. Hasil simulasi terbaik diperoleh dengan skenario penambahan operator di semua unit. Optimasi waktu tunggu pelayanan pasien dengan menggunakan pendekatan simulasi skenario terbaik ditunjukkan oleh penurunan waktu tunggu sistem antrian sebesar 1,05 jam atau 38,9% lebih cepat dari sistem yang sudah ada.Kata kunci: Optimasi Sistem, Layanan Kesehatan, Antrian, Simulasi, DMAIC Six Sigma

scholarly journals Challenges in reducing TIA clinic waiting times from 9 to 3 days in an acute Welsh hospital

2018 ◽  
Vol 7 (4) ◽  
pp. e000237
Author(s):  
Manju Krishnan ◽  
Andrew Jones ◽  
Tal Anjum ◽  
Srikanth Chenna ◽  
Peter Michael Edward Slade ◽  
...  
Keyword(s):  
Waiting Time ◽  
Positive Impact ◽  
Collaborative Leadership ◽  
Waiting Times ◽  
Team Work ◽  
Service Improvement ◽  
Ward Staff ◽  
Statistical Process ◽  
Ischaemic Attack ◽  
Mutual Agreement

A patient impact project which successfully reduced the transient ischaemic attack (TIA) clinic waiting time from 9 to 3 days in an acute Welsh hospital, revealing the challenges faced and how alternative thinking and team work improved care given to our service users. Evaluating current situation, careful planning with multiple brainstorming meetings, 4 N chart and driver diagram with change ideas laid the foundation for this service improvement. Run charts, statistical process control and Pareto charts helped to identify the issues that are hindering the progress, which when rectified, reduced the clinic waiting times. Avoiding clinic cancellations by cross covering TIA clinics with mutual agreement among consultants and redeployment of ward staff to support clinics resulted in a positive impact to the patients. The average waiting time to see a patient in TIA clinic dropped from 9 days to just 3 days as a result of this, reflecting the hard-working and proactive nature of a team following a collaborative leadership journey. The service improvement initiative for ‘avoiding clinic cancellations’ was implemented in January 2017 and has reduced our waiting times by three times. Repeat analysis by six monthly Plan Do Study Act cycles revealed that this improvement is sustained.

scholarly journals EP.WE.844Two-week bowel cancer referrals during the COVID pandemic

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Ramez Antakia ◽  
Vladimir Popa-Nimigean ◽  
Thomas Athisayaraj
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Bowel Habit ◽  
Red Flags ◽  
National Guidelines ◽  
Mean Waiting Time ◽  
Significant Difference ◽  
Immunochemical Test ◽  
Red Flag ◽  
The Impact

Abstract Aims The aims were to assess the impact of the COVID-19 pandemic on the waiting times for patients referred via the two-week pathway for suspected colorectal cancer. We also examined the use of Faecal Immunochemical Test (FIT) alongside the presenting complaints in triaging/prioritising patients for further imaging and/or endoscopic investigations appropriately. Methods A list of all patients referred via the two-week pathway to the West Suffolk Hospital for suspected colorectal cancers from 30/01/2020 to 19/07/2020 was compiled. The main four red flag symptoms were change in bowel habit (CIBH), anorectal bleeding, anaemia and weight loss. A subset of 235 patients were closely examined regarding their presenting complaints, FIT, imaging and endoscopy results with analysis of outcomes. Results 127 male versus 108 female patients were included. 59.61% of patients who were eligible for the FIT test received one. Mean waiting time for FIT positive patients was 42.39 (95% CI) versus 61.10 (95% CI) for FIT negative patients. Patients with one or two red flags symptoms had a mean waiting time of 44.81 days (95% CI 35.79-53.82) and 47.91 days (95% CI 38.07-57.75) respectively. Patients with three red flag symptoms had a mean waiting time of 28.2 days (95% CI 17.94-38.39). There was a statistically significant difference in mean waiting time between patients having 1-2 symptoms and patients with three symptoms (p < 0.005). Conclusions Despite delays during the COVID pandemic particularly for endoscopy, high risk and FIT positive patients were prioritised. Waiting times were still higher than advised national guidelines.

Improving process quality for pediatric emergency department

2014 ◽  
Vol 27 (4) ◽  
pp. 336-346 ◽  
Author(s):  
Byungjoon B.J. Kim ◽  
Theodore R. Delbridge ◽  
Dawn B. Kendrick
Keyword(s):  
Flow Control ◽  
Waiting Time ◽  
Pediatric Patients ◽  
Waiting Times ◽  
Discrete Event ◽  
Process Quality ◽  
Content Type ◽  
Arrival Times ◽  
Number Of Patients ◽  
Control Management

Purpose – Overcrowding in emergency departments (EDs) leads to longer waiting times and results in higher number of patients leaving the ED without being seen by a physician. EDs need to improve quality for patients’ waiting time and length of stay (LoS) from the perspective of process and flow control management. The paper aims to discuss these issues. Design/methodology/approach – The retrospective case study was performed using the computerized ED patient time logs from arrival to discharge between July 1, 2009 and June 30, 2010. Patients were divided into two groups either adult or pediatric with a cutoff age of 18. Patients’ characteristics were measured by arrival time periods, waiting times before being seen by a physician, total LoS and acuity levels. A discrete event simulation was applied to the comparison of quality performance measures. Findings – Statistically significant differences were found between the two groups in terms of arrival times, acuity levels, waiting time stratified for various arrival times and acuity levels. The process quality for pediatric patients could be improved by redesign of patient flow management and medical resource. Research limitations/implications – The results are limited to a case of one community and ED. This study did not analyze the characteristic of leaving the ED without being seen by a physician. Practical implications – Separation of pediatric patients from adult patients in an ED can reduce the waiting time before being seen by a physician and the total staying time in the ED for pediatric patients. It can also lessen the chances for pediatric patients to leave the ED without being seen by a physician. Originality/value – A process and flow control management scheme based on patient group characteristics may improve service quality and lead to a better patient satisfaction in ED.

scholarly journals Children Who Leave the Emergency Department Without Being Seen: Why Did They Leave and What Would Make Them Stay?

2018 ◽  
Vol 31 (2) ◽  
pp. 109 ◽  
Author(s):  
Rodrigo Sousa ◽  
Cátia Correia ◽  
Rita Valsassina ◽  
Sofia Moeda ◽  
Teresa Paínho ◽  
...  
Keyword(s):  
Emergency Department ◽  
Medical Care ◽  
Patient Outcomes ◽  
Waiting Time ◽  
Waiting Times ◽  
Adverse Outcomes ◽  
Decision To Leave ◽  
Left Without Being Seen ◽  
Number Of Patients ◽  
Control Study

Introduction: Children who visit emergency departments and leave without being seen represent a multifactorial problem. We aimed to compare the sociodemographic characteristics of children who left and of those who did not leave, as well as to evaluate parental reasoning, subsequent use of medical care and patient outcome.Material and Methods: This was a prospective case-control study of a random sample of children who left without being seen and their matched controls from an emergency department during a three-month period. We performed a phone questionnaire to obtain information concerning reasons for leaving, patient outcomes and general feedback.Results: During the study period, 18 200 patients presented to the emergency department, of whom 92 (0.5%) left without being seen. Fifty-five (59.8%) completed the questionnaire and there were 82 controls. The most common reasons for leaving were ‘excessive waiting time’ (92.7%) and ‘problem could wait’ (21.8%). A significantly higher number of patients who left sought further medical care (78.2% vs 11%) but they did not experience higher levels of unfavourable outcomes.Discussion: The waiting time seems to be the major factor that drives the decision to leave. The fact that parents felt safe in leaving and the low level of adverse outcomes highlights the low-acuity nature of the majority of patients who leave.Conclusion: Reducing the waiting times may be the logical strategic mean to decrease the rates of patients who leave without being seen. However, our data seems to indicate that the concerns surrounding clinical outcome after leaving may be partly unwarranted.

scholarly journals Predicting Patient Wait Times in the Phlebotomy Unit

2020 ◽  
pp. 1-5
Author(s):  
Dilek Orbatu ◽  
Oktay Yıldırım ◽  
Eminullah Yaşar ◽  
Ali Rıza Şişman ◽  
Süleyman Sevinç
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
High Accuracy ◽  
Wait Times ◽  
Blood Collection ◽  
Time Prediction ◽  
Number Of Patients ◽  
Patient Will ◽  
Patient Waiting Time

Patients frequently complain of long waiting times in phlebotomy units. Patients try to predict how long they will stay in the phlebotomy unit according to the number of patients in front of them. If it is not known how fast the queue is progressing, it is not possible to predict how long a patient will wait. The number of prior patients who will come to the phlebotomy unit is another important factor that changes the waiting time prediction. We developed an artificial intelligence (AI)-based system that predicts patient waiting time in the phlebotomy unit. The system can predict the waiting time with high accuracy by considering all the variables that may affect the waiting time. In this study, the blood collection performance of phlebotomists, the duration of the phlebotomy in front of the patient, and the number of prior patients who could come to the phlebotomy unit was determined as the main parameters affecting the waiting time. For two months, actual wait times and predicted wait times were compared. The wait time for 95 percent of the patients was predicted with a variance of ± 2 minutes. An AI-based system helps patients make predictions with high accuracy, and patient satisfaction can be increased.

Measuring and Managing Waiting Times: What's to Be Done?

2002 ◽  
Vol 15 (2) ◽  
pp. 6-10 ◽  
Author(s):  
Carolyn DeCoster
Keyword(s):  
Information System ◽  
Data Collection ◽  
Waiting Time ◽  
Waiting Times ◽  
September 11 ◽  
Collection System ◽  
Data Collection System ◽  
Treatment Services ◽  
Time Information ◽  
The Ideal

On September 11, 2000, the First Ministers of Canada issued a communiqué pledging to develop and report on waiting times for a number of diagnostic and treatment services. Reporting is to begin by September 2002. Given this commitment, what are the ideal characteristics of such a data collection system? This article defines and evaluates methods of measuring waiting times, and recommends a prioritized waiting-time information system to permit both measurement and management.

Cutting cancer waiting times: streamlining cervical lymph node biopsy

2013 ◽  
Vol 127 (10) ◽  
pp. 1007-1011 ◽  
Author(s):  
G Dimbleby ◽  
L Golding ◽  
O Al Hamarneh ◽  
I Ahmad
Keyword(s):  
Lymph Node ◽  
Waiting Time ◽  
Cervical Lymph Node ◽  
Waiting Times ◽  
Lymph Node Biopsy ◽  
Interquartile Range ◽  
National Guidelines ◽  
Node Biopsy ◽  
Retrospective Audit ◽  
Cervical Lymph Node Biopsy

AbstractBackground:Patients with enlarged lymph nodes present to a number of different specialties and diagnosis is often made following a biopsy.Objective:This study aimed to establish department waiting times for cervical lymph node biopsy, and compare these to the cancer services guidelines.Methods:A retrospective audit was carried out to record patient waiting times (defined as the number of days from referral to biopsy) between May and December 2010. A proforma for referral was introduced. In addition, appointments for biopsies were arranged by a co-ordinator. A prospective re-audit was carried out from March to September 2011.Results:The first audit showed that national guidelines were not met; there was a median waiting time of 74 days (interquartile range, 47–113). Re-audit demonstrated a significant reduction in waiting times using the proforma; the median waiting time had decreased to 18 days (interquartile range, 9–22).Conclusion:A proforma for lymph node biopsy and a designated co-ordinator streamlined the service, significantly reducing waiting times. Together, these can aid referral for meeting guidelines and improve patient care.

scholarly journals An audit of waiting times in the outpatient clinic in Inverness Sector A NHS Highland during the COVID-19 pandemic

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S113-S113
Author(s):  
Oksana Zinchenko ◽  
Jennifer Hyland
Keyword(s):  
Mental Health ◽  
Waiting Time ◽  
Waiting Times ◽  
Health Assessment ◽  
Outpatient Service ◽  
Bimodal Distribution ◽  
Patient Record ◽  
Mental Health Assessment ◽  
Mean Waiting Time ◽  
Number Of Patients

AimsThis audit was to assess and improve the organizational efficiency of referrals to Inverness Sector A Outpatient Service. The referrals were audited to measure the average waiting time from referral to first offered outpatient appointment and to assess the proportion of patients waiting longer than 12 weeks.MethodThe audit included routine referrals to the CMHT Inverness Sector A, NHS Highland from GP practices: Kingsmills, Burnfield, Riverside, Fairfield, Foyers and Drumnadrochit Medical Practices. The number of referrals and the number and proportion of clients given appointments for assessments were calculated. Referrals were received directly from primary care and the Mental Health Liaison Team or following Out of Hours contacts at the Mental Health Assessment Team.Data were collected retrospectively: referrals from 1 Jan 2020–31 Aug 2020. Sample size came to 160 patients aged 16–65 years. Data were collected via review of recorded documentation on the NHSH electronic patient record systems (SCIstore), from 5th–25th January 2021.Result160 patients (male 82, female 78) were referred from 1 Jan to 1 Sept 2020. Of these, 140 (87.5%) were given an appointment for an assessment. The mean waiting time was 12 weeks for 103 patients (64%), with 57 patients (36%) waiting longer than 13 weeks. The bimodal distribution of waiting times prompted an analysis of those with longer waiting times. In some instances, appointments were delayed because patients either did not attend (DNA) or cancelled their appointments. Reasons for delays included: postponement until further information was available; cancellation of meetings or patients DNA. In 20 cases (12.5%), the referrals deemed inadequate, prompting further liaison with the referrer for clarification about the nature of the problem and previous psychological interventions.ConclusionThe number of transactions (any amendment to a patient record) was higher than the number of patients affected, as several transactions can relate to one patients’ record.Most referrals are vetted in advance via the daily Inverness triage huddle. Ways of improving the quality of information provided by referrers would be explored.On receipt of each referral, the date of the 12 week deadline would be calculated and highlighted in a database.The cross-sector (Highland wide) standardisation will add clarity about medical capacity, that does not involve use of excessive clinician time.

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