scholarly journals Telehealth to support referral management in a universal health system: a before-and-after study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sabrina Dalbosco Gadenz ◽  
Josué Basso ◽  
Patrícia Roberta Berithe Pedrosa de Oliviera ◽  
Stephan Sperling ◽  
Marcus Vinicius Dutra Zuanazzi ◽  
...  
Keyword(s):  
Linear Models ◽  
Patient Flow ◽  
Waiting Times ◽  
Median Number ◽  
Waiting Lists ◽  
Referral Management ◽  
Before And After ◽  
The Impact ◽  
Universal Health ◽  
Before And After Study

Abstract Background Management of patient flow within a healthcare network, allowing equitable and qualified access to healthcare, is a major challenge for universal health systems. Implementation of telehealth strategies to support referral management has been shown to increase primary care resolution and to promote coordination of care. The objective of this study was to assess the impact of telehealth strategies on waiting lists and waiting times for specialized care in Brazil. Methods Before-and-after study with measures obtained between January 2019 and February 2020. Baseline measurements of waiting lists were obtained immediately before the implementation of a remotely operated referral management system. Post-interventional measurements were obtained monthly, up to six months after the beginning of operation. Data was extracted from the database of the project. General linear models were applied to assess interaction of locality and time over number of cases on waiting lists and waiting times. Results At baseline, the median number of cases on waiting lists ranged from 2961 to 12,305 cases. Reductions of the number of cases on waiting lists after six months of operation were observed in all localities. The magnitude of the reduction ranged from 54.67 to 88.97 %. Interaction of time measurements was statistically significant from the second month onward. Median waiting times ranged from 159 to 241 days at baseline. After six months, there was a decrease of 100 and 114 waiting days in two localities, respectively, with reduction of waiting times only for high-risk cases in the third locality. Conclusions Adoption of telehealth strategies resulted in the reduction of number of cases on waiting lists. Results were consistent across localities, suggesting that telehealth interventions are viable in diverse settings.

scholarly journals Impact of the pan-Canadian Oncology Drug Review on provincial concordance with respect to cancer drug funding decisions and time to funding

2017 ◽  
Vol 24 (5) ◽  
pp. 295 ◽  
Author(s):  
A. Srikanthan ◽  
H. Mai ◽  
N. Penner ◽  
E. Amir ◽  
A. Laupacis ◽  
...  
Keyword(s):  
Linear Models ◽  
Drug Product ◽  
Median Number ◽  
Cancer Drug ◽  
Cancer Drugs ◽  
Oncology Drug ◽  
Before And After ◽  
Canadian Provinces ◽  
The Impact ◽  
Drug Review

Background The pan-Canadian Oncology Drug Review (pcodr) was implemented in 2011 to address uneven drug coverage and lack of transparency with respect to the various provincial cancer drug review processes in Canada. We evaluated the impact of the pcodr on provincial decision concordance and time from Notice of Compliance (noc) to drug funding.Methods In a retrospective review, Health Canada’s Drug Product Database was used to identify new indications for cancer drugs between January 2003 and May 2014, and provincial formulary listings for drug-funding dates and decisions between 1 January 2003 and 31 December 2014 were retrieved. Multiple linear models and quantile regressions were used to evaluate changes in time to decision-making before and after the implementation of the pcodr. Agreement of decisions between provinces was evaluated using kappa statistics.Results Data were available from 9 provinces (all Canadian provinces except Quebec), identifying 88 indications that represented 51 unique cancer drugs. Two provinces lacked available data for all 88 indications at the time of data collection. Interprovincial concordance in drug funding decisions significantly increased after the pcodr’s implementation (Brennan-Prediger coefficient: 0.54 pre-pcodr vs. 0.78 post-pcodr; p = 0.002). Nationwide, the median number of days from Health Canada’s noc date to the date of funding significantly declined (to 393 days from 522 days, p < 0.001). Exploratory analyses excluding provinces with incomplete data did not change the results.Conclusions After the implementation of the pcodr, greater concordance in cancer drug funding decisions between provinces and decreased time to funding decisions were observed.

scholarly journals Robust Inferences from a Before-and-After Study with Multiple Unaffected Control Groups

2013 ◽  
Vol 1 (2) ◽  
pp. 209-234 ◽  
Author(s):  
Pengyuan Wang ◽  
Mikhail Traskin ◽  
Dylan S. Small
Keyword(s):  
Sensitivity Analysis ◽  
Control Groups ◽  
Two Stage ◽  
Time Effects ◽  
Unaffected Control ◽  
Group Time ◽  
Before And After ◽  
Group Variation ◽  
The Impact ◽  
Before And After Study

AbstractThe before-and-after study with multiple unaffected control groups is widely applied to study treatment effects. The current methods usually assume that the control groups’ differences between the before and after periods, i.e. the group time effects, follow a normal distribution. However, there is usually no strong a priori evidence for the normality assumption, and there are not enough control groups to check the assumption. We propose to use a flexible skew-t distribution family to model group time effects, and consider a range of plausible skew-t distributions. Based on the skew-t distribution assumption, we propose a robust-t method to guarantee nominal significance level under a wide range of skew-t distributions, and hence make the inference robust to misspecification of the distribution of group time effects. We also propose a two-stage approach, which has lower power compared to the robust-t method, but provides an opportunity to conduct sensitivity analysis. Hence, the overall method of analysis is to use the robust-t method to test for the overall hypothesized range of shapes of group variation; if the test fails to reject, use the two-stage method to conduct a sensitivity analysis to see if there is a subset of group variation parameters for which we can be confident that there is a treatment effect. We apply the proposed methods to two datasets. One dataset is from the Current Population Survey (CPS) to study the impact of the Mariel Boatlift on Miami unemployment rates between 1979 and 1982.The other dataset contains the student enrollment and grade repeating data in West Germany in the 1960s with which we study the impact of the short school year in 1966–1967 on grade repeating rates.

AIRA, an Artificial Intelligence Realistic Assistant that will save your time, a before-and-after study during Pandemic (Preprint)

2021 ◽  
Author(s):  
Kaio Bin ◽  
Adler Araújo Ribeiro Melo ◽  
José Guilherme Franco Da Rocha ◽  
Renata Pivi De Almeida ◽  
Vilson Cobello Junior ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Medical Care ◽  
Health Screening ◽  
Urgent Care ◽  
Health Record ◽  
Before And After ◽  
Electronic Health ◽  
The Impact ◽  
Before And After Study

BACKGROUND AIRA is an AI designed to reduce the time that doctors dedicate filling out EHR, winner of the first edition of MIT Hacking Medicine held in Brazil in 2020. As a proof of concept, AIRA was implemented in administrative process before its application in a medical process. OBJECTIVE The aim of the study is to determinate the impact of AIRA by eliminating the Medical Care Registration (MCR) on Electronic Health Record (EHR) by Administrative Officer. METHODS This is a comparative before-and-after study following the guidance “Evaluating digital health products” from Public Health England. An Artificial Intelligence named AIRA was created and implemented at CEAC (Employee Attention Center) from HCFMUSP. A total of 25,507 attendances were evaluated along 2020 for determinate AIRA´s impact. Total of MCR, time of health screening and time between the end of the screening and the beginning of medical care, were compared in the pre and post AIRA periods. RESULTS AIRA eliminated the need for Medical Care Registration by Administrative Officer in 92% (p<0.0001). The nurse´s time of health screening increased 16% (p<0.0001) during the implementation, and 13% (p<0.0001) until three months after the implementation, but reduced in 4% three months after implementation (p<0.0001). The mean and median total time to Medical Care after the nurse’ Screening was decreased in 30% (p<0.0001) and 41% (p<0.0001) respectively. CONCLUSIONS The implementation of AIRA reduced the time to medical care in an urgent care after the nurse´ screening, by eliminating non-value-added activity the Medical Care Registration on Electronic Health Record (EHR) by Administrative Officer.

scholarly journals 888. Improving outcomes with revised preoperative universal decolonization protocol

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S479-S479
Author(s):  
Silvia I Gnass
Keyword(s):  
Surgical Procedures ◽  
Baseline Period ◽  
Surgical Team ◽  
Team Members ◽  
Surgical Infection ◽  
Before And After ◽  
Trial Basis ◽  
The Impact ◽  
Before And After Study ◽  
Patient Infection

Abstract Background In order to improve outcomes, including reduced surgical infection rate and costs, a revised universal preoperative decolonization protocol was implemented on a trial basis. Methods In a 12 month before and after study at a public teaching hospital in southern California, an alcohol based nasal antiseptic was introduced in place of nasal povidone iodine (PVI) for all surgical patients pre-operatively, paired with chlorhexidine (CHG) bathing which was already in place. All surgical procedures were included, the most common being cholecystectomy, cesarean section and hip fracture. The alcohol nasal antiseptic was selected to replace the PVI nasal antiseptic based on efficacy, staff preference and cost. At the same time, surgical team members began self-application of the alcohol nasal antiseptic each day prior to surgical procedures. This was not mandatory and compliance was not tracked, though informal feedback and observation revealed most surgical team members were applying the nasal antiseptic prior to cases daily. Results In comparison to the 6 month baseline period where there were 27 SSI in 1188 procedures, during the 6 month study period there were 10 SSI in 1253 procedures, representing a 63% reduction (p=.0162) for all types of procedures. We have observed a reduction of 17 SSIs in 2019, compared to the previous year, during the 6 months period. That means a saving of $589,420 during the same period. Conclusion Preoperative universal decolonization with alcohol based nasal antiseptic in place of nasal PVI, paired with CHG bathing, was effective in reducing SSI rate and associated costs. Further study is needed to measure and assess the impact of surgical team member nasal decolonization on patient infection risk and rate. Disclosures All Authors: No reported disclosures

scholarly journals Robust capacity planning for accident and emergency services

2020 ◽  
Vol 54 (6) ◽  
pp. 1757-1773
Author(s):  
Elvan Gökalp
Keyword(s):  
Capacity Planning ◽  
Emergency Services ◽  
Simulation Optimization ◽  
Patient Flow ◽  
Waiting Times ◽  
Model Parameters ◽  
Planning Problem ◽  
Accident And Emergency ◽  
Number Of Patients ◽  
The Impact

Accident and emergency departments (A&E) are the first place of contact for urgent and complex patients. These departments are subject to uncertainties due to the unplanned patient arrivals. After arrival to an A&E, patients are categorized by a triage nurse based on the urgency. The performance of an A&E is measured based on the number of patients waiting for more than a certain time to be treated. Due to the uncertainties affecting the patient flow, finding the optimum staff capacities while ensuring the performance targets is a complex problem. This paper proposes a robust-optimization based approximation for the patient waiting times in an A&E. We also develop a simulation optimization heuristic to solve this capacity planning problem. The performance of the approximation approach is then compared with that of the simulation optimization heuristic. Finally, the impact of model parameters on the performances of two approaches is investigated. The experiments show that the proposed approximation results in good enough solutions.

scholarly journals The impact of computerized provider order entry on emergency department flow

CJEM ◽  
2016 ◽  
Vol 18 (4) ◽  
pp. 264-269 ◽  
Author(s):  
Andrew Gray ◽  
Christopher M.B. Fernandes ◽  
Kristine Van Aarsen ◽  
Melanie Columbus
Keyword(s):  
Emergency Department ◽  
Patient Safety ◽  
Improve Patient Safety ◽  
Patient Flow ◽  
Wait Time ◽  
Order Entry ◽  
Computerized Provider Order Entry ◽  
Left Without Being Seen ◽  
Before And After ◽  
The Impact

AbstractObjectivesComputerized provider order entry (CPOE) has been established as a method to improve patient safety by avoiding medication errors; however, its effect on emergency department (ED) flow remains undefined. We examined the impact of CPOE implementation on three measures of ED throughput: wait time (WT), length of stay (LOS), and the proportion of patients that left without being seen (LWBS).MethodsWe conducted a retrospective cohort study of all ED patients of 18 years and older presenting to London Health Sciences Centre during July and August 2013 and 2014, before and after implementation of a CPOE system. The three primary variables were compared between time periods. Subgroup analyses were also conducted within each Canadian Triage and Acuity Scale (CTAS) level (1–5) individually, as well as for admitted patients only.ResultsA significant increase in WT of 5 minutes (p=0.036) and LOS of 10 minutes (p=0.001), and an increase in LWBS from 7.2% to 8.1% (p=0.002) was seen after CPOE implementation. Admitted patients’ LOS increased by 63 minutes (p<0.001), the WT of CTAS 3 and 5 patients increased by 6 minutes (p=0.001) and 39 minutes (p=0.005), and LWBS proportion increased significantly for CTAS 3–5 patients, from 24.3% to 42.0% (p<0.001) for CTAS 5 patients specifically.ConclusionsCPOE implementation detrimentally impacted all patient flow throughput measures that we examined. The most striking clinically relevant result was the increase in LOS of 63 minutes for admitted patients. This raises the question as to whether the potential detrimental effects to patient safety of CPOE implementation outweigh its benefits.

scholarly journals mHealth App Prescription in Australian General Practice: Pre-Post Study

10.2196/16497 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e16497 ◽  
Author(s):  
Oyungerel Byambasuren ◽  
Elaine Beller ◽  
Tammy Hoffmann ◽  
Paul Glasziou
Keyword(s):  
General Practice ◽  
Median Number ◽  
Implementation Intervention ◽  
Electronic Reporting ◽  
Before And After ◽  
Australian General Practice ◽  
Prescription Rates ◽  
Drug Interventions ◽  
The Impact ◽  
Mhealth Apps

Background Evidence of effectiveness of mobile health (mHealth) apps as well as their usability as non-drug interventions in primary care are emerging around the globe. Objective This study aimed to explore the feasibility of mHealth app prescription by general practitioners (GPs) and to evaluate the effectiveness of an implementation intervention to increase app prescription. Methods A single-group, before-and-after study was conducted in Australian general practice. GPs were given prescription pads for 6 mHealth apps and reported the number of prescriptions dispensed for 4 months. After the reporting of month 2, a 2-minute video of one of the apps was randomly selected and sent to each GP. Data were collected through a prestudy questionnaire, monthly electronic reporting, and end-of-study interviews. The primary outcome was the number of app prescriptions (total, monthly, per GP, and per GP per fortnight). Secondary outcomes included confidence in prescribing apps (0-5 scale), the impact of the intervention video on subsequent prescription numbers, and acceptability of the interventions. Results Of 40 GPs recruited, 39 commenced, and 36 completed the study. In total, 1324 app prescriptions were dispensed over 4 months. The median number of apps prescribed per GP was 30 (range 6-111 apps). The median number of apps prescribed per GP per fortnight increased from the pre-study level of 1.7 to 4.1. Confidence about prescribing apps doubled from a mean of 2 (not so confident) to 4 (very confident). App videos did not affect subsequent prescription rates substantially. Post-study interviews revealed that the intervention was highly acceptable. Conclusions mHealth app prescription in general practice is feasible, and our implementation intervention was effective in increasing app prescription. GPs need more tailored education and training on the value of mHealth apps and knowledge of prescribable apps to be able to successfully change their prescribing habits to include apps. The future of sustainable and scalable app prescription requires a trustworthy electronic app repository of prescribable mHealth apps for GPs.

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