scholarly journals Impact of artificial intelligence on waiting time for medical care in an urgent care service for Covid-19. (Preprint)

10.2196/29012 ◽  
2021 ◽  
Author(s):  
Kaio Bin ◽  
Adler Araújo Ribeiro Melo ◽  
José Guilherme Franco Da Rocha ◽  
Renata Pivi De Almeida ◽  
Vilson Cobello Jr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Care ◽  
Waiting Time ◽  
Care Service ◽  
Urgent Care

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 Alcohol intoxication: evaluation of the population appealability and emergency medical care in Kazan

2017 ◽  
Vol 98 (2) ◽  
pp. 243-247
Author(s):  
V L Paykov ◽  
E I Zamaleeva ◽  
D A Zhukov ◽  
O L Chernova
Keyword(s):  
Medical Care ◽  
Emergency Care ◽  
Alcohol Intoxication ◽  
Care Service ◽  
Medical Personnel ◽  
Urgent Care ◽  
Emergency Medical Care ◽  
Calendar Time ◽  
Emergency Medical ◽  
Medical Care Service

Aim. To study population appealability for emergency medical care with alcohol intoxication as well as the features of medical care service for them in Kazan at modern stage. Methods. The data from emergency call cards from 2015 with the diagnosis «alcohol intoxication» (form No.11/u) were studied. A survey of 271 responders (medical personnel of mobile teams of emergency care and admission departments of the hospitals) of medical care service for people with alcohol intoxication in the streets was performed. Results. In the structure of performed calls for adult popultion the ratio of patients who called an ambulance because of alcohol intoxication was 2.1% and because of the need for urgent care - 5.7%. Males were more prevalent than females: 82.1 and 17.9% respectively. Predominantly people younger than 60 years appealed: among males 82.7%, among females - 79%. Maximum appealability was registered in July (7.4 calls per 10 000 adults); during the week - on Saturday (11.9 per 10 000 adults), and during the day - during the period from 5 to 6 pm. The survey of the teams of ambulances and admission departments demonstrated the need for re-establishment of medical sobering-up stations (83.5 and 80% respectively) and more rarely the responders suggested development of specialized medical departments and active delivery of people with alcohol intoxication to specialized institutions involving law enforcement officials and personnel of specialized sobering-up stations (13 and 14.3% respectively). Conclusion. In the structure of the calls performed by emergency care stations the ratio of patients who called an ambulance because of alcohol intoxication among adults was 2.1% and because of the need for urgent care - 5.7%; the appealability was affected by sex, age and calendar time; analysis of the survey results demonstrated the need for re-establishment of recently closed medical sobering-up stations and for development of specialized medical departments.

scholarly journals Factors Contributing to Low Adherence to Community-Based Health Insurance in Rural Nyanza District, Southern Rwanda

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Mecthilde Mukangendo ◽  
Manasse Nzayirambaho ◽  
Regis Hitimana ◽  
Assumpta Yamuragiye
Keyword(s):  
Health Insurance ◽  
Medical Care ◽  
Waiting Time ◽  
Care Provider ◽  
Care Service ◽  
Health Care Service ◽  
Community Based ◽  
Cross Sectional ◽  
Premium Payment ◽  
Community Based Health Insurance

Background. Community-based health insurance (CBHI) schemes are an emerging mechanism for providing financial protection against health-related poverty. In Rwanda, CBHI is being implemented across the country, and it is based on four socioeconomic categories of the “Ubudehe system”: the premiums of the first category are fully subsidized by government, the second and third category members pay 3000 frw, and the fourth category members pay 7000 frw as premium. However, low adherence of community to the scheme since 2011 has not been sufficiently studied. Objective. This study aimed at determining the factors contributing to low adherence to the CBHI in rural Nyanza district, southern Rwanda. Methodology. A cross-sectional study was conducted in nine health centers in rural Nyanza district from May 2017 to June 2017. A sample size of 495 outpatients enrolled in CBHI or not enrolled in the CBHI scheme was calculated based on 5% margin of error and a 95% confidence interval. Logistic regression was used to identify the determinants of low adherence to CBHI. Results. The study revealed that there was a significant association between long waiting time to be seen by a medical care provider and between health care service provision and low adherence to the CBHI scheme (P value < 0.019) (CI: 0.09107 to 0.80323). The estimates showed that premium not affordable (P value < 0.050) (CI: 0.94119 to 9.8788) and inconvenient model of premium payment (P value < 0.001) (CI: 0.16814 to 0.59828) are significantly associated with low adherence to the CBHI scheme. There was evidence that the socioeconomic status as measured by the category of Ubudehe (P value < 0.005) (CI: 1.4685 to 8.93406) increases low adherence to the CBHI scheme. Conclusion. This study concludes that belonging to the second category of the Ubudehe system, long waiting time to be seen by a medical care provider and between services, premium not affordable, and inconvenient model of premium payment were significant predictors of low adherence to CBHI scheme.

scholarly journals Patient’s Utility for Various Attributes of Diabetes Care Services

2018 ◽  
Vol 8 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Vinaytosh Mishra ◽  
Cherian Samuel ◽  
S. K. Sharma
Keyword(s):  
Health Care ◽  
Waiting Time ◽  
Diabetes Care ◽  
Care Service ◽  
Group Discussion ◽  
Health Care Service ◽  
Diabetic Patients ◽  
Relative Importance ◽  
Care Services

Diabetes is rising like an epidemic in India. The prevalence of diabetes in India has reached an alarming level of 72.95 millions. The purpose of this article is to assess the relative importance of various health care service attributes in diabetes care. Our study uses secondary research and focus group discussion to identify the attributes of a diabetes specialty clinic. The attributes included in the questionnaire were the quality of the care provide by the health care givers, spend per visit, hospitalization expense, waiting time and the distance to the hospital. Conjoint analysis was used to assess the relative importance of the attributes. It was found that the hospital’s quality was the most important attribute while the distance to the hospital was the attribute with the least importance. Although the quality of the hospital is the most important criterion in selecting a hospital in diabetes care, factors like waiting time, spend per visit, and hospitalization expense play an important role in the selection. We assess the relative importance of these factors for the diabetic patients in India. The study is first of its kind and could help policy makers in designing better health care services in diabetes care.

scholarly journals Client Satisfaction on Antenatal Care Service by Pregnant Women in Public and Private Hospitals in Addis Ababa, Ethiopia: A Cross-sectional Comparative Descriptive Study

2020 ◽  
Vol 4 (1) ◽  
Keyword(s):  
Antenatal Care ◽  
Pregnant Women ◽  
Waiting Time ◽  
Client Satisfaction ◽  
Addis Ababa ◽  
Care Service ◽  
Public Hospitals ◽  
Private Hospitals ◽  
Public And Private ◽  
Public And Private Hospitals

Background: Antenatal care (ANC) is an important health care service which is intended to potentially reduce maternal morbidity and mortality particularly in areas where the general health status of women is presumed poor, choice of facilities is limited and the service delivery compromised by geography (terrain, transport), socio-demographic factors, financial capability and awareness. Though improving the quality of health care is one of the targeted strategies in the Health Sector Development Program IV (HSDP IV) of Ethiopia, little is known about the quality of antenatal care service and client satisfaction at the different hospitals in Addis Ababa, the capital city of Ethiopia. Objective: To determine satisfaction of ANC services among pregnant women at the public teaching and private hospitals in Addis Ababa, Ethiopia. Methods: Health institution-based comparative cross-sectional study was conducted from February to June, 2019 in public and private hospitals, in Addis Ababa, using sample size determination for comparisons of proportion between the two populations. All participants who fulfilled the inclusion criteria were enrolled based on the flow of pregnant women to the ANC clinics at the selected hospitals. Data were entered and cleaned using EPI-info version 3.5.1 and analysis was performed by SPSS version 21. Association of independent variables with the client satisfaction was done using binary and multivariate logistic regression. Significant association of variables with outcome was determined using adjusted odds ratio (AOR) together with 95 % confidence interval. Level of significance was set at P-value of ≤ 0.05. Results: Five hundred seventy one pregnant women attending Antenatal Care at private (281) and public (290) hospitals were included with response rates of 94.1 and 91.2% for public and private hospitals, respectively. The age distribution of the participants was between 17 and 43 years with a mean age of 27.3±5.1 years. Most of the clients, 249 (88.7%) at private and 276 (95.2%) at public hospitals were between the ages of 20 and 34 years. One hundred fourteen (39.3%) of the clients at public and 113 (40.2%) at private hospitals were nulliparous. The clients overall satisfaction with antenatal care was mostly positive both at the private and public hospitals and two hundred twenty eight (81.1%) of the private and 174 (60%) of the public hospitals were satisfied with the services provided. Having ANC follow up at the private hospitals had statistically significant difference in client satisfaction compared to those in public hospitals with P value of 0.019, (AOR 2.97, 95% CI:1.19 -7.74). Clients’ satisfaction with the cleanliness of the environment was 11.1 times more likely to be satisfied with the general ANC service, P<0.05, (AOR 12.18 95% CI: 7.45-19.91). Having more than 4 ANC visits was positively associated with client overall satisfaction, P= 0.021, (AOR 2.41, 95% CI: 1.12-5.24,) while long waiting time is negatively associated with client satisfaction. Conclusions: The study showed significant difference in client satisfaction rate between the selected private and public facilities. Private facilities outperformed public facilities with regards to structural features (privacy, waiting time, space, and neatness). We recommend concerted effort to improve ANC visits and pay due attention to the privacy, waiting time, and the neatness of the facilities in public hospitals.

scholarly journals Preference for artificial intelligence medicine after COVID-19 pandemic: Discrete choice experiment (Preprint)

2021 ◽  
Author(s):  
Wai-Kit Ming ◽  
Taoran Liu ◽  
Winghei Tsang ◽  
Yifei Xie ◽  
Kang Tian ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Health System ◽  
Waiting Time ◽  
Latent Class ◽  
Public Health System ◽  
Individual Preference ◽  
Female Respondent ◽  
Reference Level ◽  
The Public

BACKGROUND The COVID-19 pandemic poses a great threat to the public health system globally and has squeezed medical and doctor resources. Artificial intelligence (AI) has potential uses in virus detection and relieving the public health pressure caused by the pandemic. In the case of a shortage of medical resources caused by the pandemic, whether people’s preference for AI doctors and traditional clinicians has changed is worth exploring. OBJECTIVE We aim to quantify and compare people’s preference for AI medicine and traditional clinicians before and after the COVID-19 pandemic to check whether people’s preference is affected by the pressure of pandemic METHODS The propensity score matching (PSM) method was applied to match two different groups of respondents recruited in 2017 and 2020 with similar demographic characteristics. A total of 2048 respondents (1520 from 2017 and 528 from 2020) completed the questionnaire and were included in the analysis. The Multinomial Logit Model (MNL) and Latent Class Model (LCM) were used to explore people’s preferences for different diagnosis methods. RESULTS Among these respondents, 84.7% in 2017 and 91.3% in 2020 were confident that AI diagnosis would outperform human clinician diagnoses in the future. Both groups of respondents matched from 2017 and 2020 attached most importance to the attribute ‘accuracy’, followed by ‘diagnosis expense’, and they prefer the combined diagnosis of AI and human clinicians (2017: odds ratio [OR] 1.645; 95% CI 1.535,1.763, p < 0.001; 2020: OR 1.513, 95% CI 1.413, 1.621, p < 0.001, Reference level: Clinician). LCM identified three classes with different attribute priorities. In Class 1, the preference for combination diagnosis and accuracy remains constant in 2017 and 2020, and higher accuracy (e.g., 2017 OR for 100% 1.357; 95% CI 1.164, 1.581) is preferred. People in 2017 and 2020 prefer 0 min outpatient waiting time and 0 RMB diagnosis expense. In Class 2, the 2017 matched data is also very similar to class 2 in 2020, AI combined with human clinicians (2017: OR 1.204, 95% CI 1.039, 1.394, p = 0.011; 2020: OR 2.009, 95% CI 1.826, 2.211, p < 0.001, Reference level: Clinician) and 20 minutes (2017: OR 1.349, 95% CI 1.065, 1.708, p < 0.001; 2020: OR 1.488, 95% CI 1.287, 1.721, p < 0.001, Reference level, 0 min) of outpatient waiting time were consistently preferred. In Class 3, the respondents in 2017 and 2020 had different preferences for diagnosis method; respondents in Class 3 of 2017 prefer clinicians, whereas respondents in Class 3 of 2020 prefer AI diagnosis. The odds ratios of accuracy continued increasing with the increasing of accuracy, like other classes of 2017 and 2020. As for the latent class segmented according to different sexes, all of the male and female respondent classes from 2017 and 2020 rank accuracy as the most important attribute. CONCLUSIONS Individual preference for clinical diagnosis between AI and human clinicians were very similar and mostly unaffected by the burden of the public health system caused by the pandemic. Diagnosis accuracy and expense for diagnosis were of the most important attributes of choice of the type of diagnosis. These findings can provide guidance for policymaking relevant to the development of AI-based healthcare.

Enhancing data-driven elderly appointment services in domestic care communities under COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
H.Y. Lam ◽  
G.T.S. Ho ◽  
Daniel Y. Mo ◽  
Valerie Tang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Care Service ◽  
The Elderly ◽  
Care Centre ◽  
Service Matching ◽  
Content Type ◽  
Care Services ◽  
Domestic Care

PurposeUnder the impact of Coronavirus disease 2019 (COVID-19), this paper contributes in the deployment of the Artificial Intelligence of Things (AIoT)-based system, namely AIoT-based Domestic Care Service Matching System (AIDCS), to the existing electronic health (eHealth) system so as to enhance the delivery of elderly-oriented domestic care services.Design/methodology/approachThe proposed AIDCS integrates IoT and Artificial Intelligence (AI) technologies to (1) capture real-time health data of the elderly at home and (2) provide the knowledge support for decision making in the domestic care appointment service in the community.FindingsA case study was conducted in a local domestic care centre which provided elderly oriented healthcare services to the elderly. By integrating IoT and AI into the service matching process of the mobile apps platform provided by the local domestic care centre, the results proved that customer satisfaction and the quality of the service delivery were improved by observing the key performance indicators of the transactions after the implementation of the AIDCS.Originality/valueFollowing the outbreak of COVID-19, this is a new attempt to overcome the limited research done on the integration of IoT and AI techniques in the domestic care service. This study not only inherits the ability of the existing eHealth system to automatically capture and monitor the health status of the elderly in real-time but also improves the overall quality of domestic care services in term of responsiveness, effectiveness and efficiency.

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