Scheduled Medical Appointments Research Articles

Medical Appointment Adherence and Challenges Encountered by HIV Infected Children at Kenyatta National Hospital, Kenya

Purpose: The goal of this study was to determine the adherence and challenges to scheduled medical appointment on HIV care services among HIV positive children aged 18 months to nine years undergoing treatment at Kenyatta National Hospital, Kenya at six months interval
 Methodology: The study design was prospective cohort study design consisting of 221 caregivers and their HIV infected children. Consecutive sampling method was used, in which potential participants were sought out on a daily basis until we had contacted all the eligible individuals and enroled them.This research was undertaken from March, 2017 to November, 2018. The study participants each was followed for period of one year. Pretested questionnaire was used for collecting data together with review of standardized clinical notes on challenges and adherence to scheduled medical appointments. Data was analyzed using statistical package for the Social Sciences (SPSS) Version 20. The descriptive statistics were done and data was presented in frequency tables. Ethical approval no P688/09/2016) was obtained.
 Findings: It was observed that adherence to scheduled medical appointment among children infected with HIV ranged from 77% to 81.9% at 6 and 12 months respectively. This study found an average of 20% of children infected with HIV are at high risk of adverse outcomes related to non-adherence to planned medical appointments. The observed difference on adherence was associated with financial constrain, perceived stigma and discrimination, long distance and long waiting period in the medical HIV clinic.
 Unique Contribution to Theory, Practice and Policy: Targeted intervention aimed at addressing measures to reduce children missing attendance of scheduled medical appointment is vital. The health care providers may address measures to reduce children missing attendance of scheduled medical appointment by reducing waiting time in the clinic and reducing the number of scheduled medical appointments. This study adds to the pool of knowledge that stigma and discrimination, long waiting time in the clinic and the number of scheduled medical appointments has influence on adherence to appointments. The attendance of scheduled medical appointment should be enhanced in the policies by Ministry of health and HIV health care providers focusing on reducing waiting time, mitigating stigma and discrimination.

Patient profile and reasons for the absence from scheduled medical appointments

Organizing health services is a challenge for managers. Moreover, patients’ absenteeism to appointments is a relevant issue. This study aims to evaluate the profile of absent users, the medical specialty, and reasons for absence from medical consultations in a reference clinic. This is a cross-sectional study conducted at a tertiary hospital in the state of São Paulo. Data was obtained from reports from the Hospital Center for Medical Informatics and electronic patient records (from January 1st to December 31st, 2018). Structured interviews were carried out (from April 2018 to February 2019) by phone with a random sample of 317 patients who were aged 18 years old or above and missed consultations in twelve medical specialties, after approval by the Research Ethics Committee. Analysis were performed using descriptive and analytical statistics, with a significance level set at 5%. In total, 69.09% of patients say they missed consultations. It is noteworthy that 18.06% of patients said they attended consultations, followed by those who claimed to be sick or hospitalized (16.98%, p-value=0.0006). Analyzing the context and reasons for absences generates knowledge for service management. We found inadequacies in scheduling and system records. Patients and health services must be co-responsible for optimizing the use of public health resources.

Competitive Algorithms for the Online Minimum Peak Job Scheduling

Algorithms to schedule medical appointments This paper was inspired by a field collaboration effort to develop and disseminate a real-time appointment scheduling decision support tool for an outpatient cancer infusion center in a large healthcare system. Two challenging aspects of scheduling daily medical appointments are that each patient is scheduled upon arrival without knowledge on future patients and that the appointments typically consume scarce physical resources (e.g., chairs, nurses, and doctors). A desirable schedule should have relatively smooth utilization over the course of a day to minimize the peak demand for the scarce resources. This paper develops new real-time (online) algorithms to schedule appointments in medical and other settings. It establishes theoretical properties of these algorithms, showing that they perform close to algorithms that could exploit full retrospective information on all the appointments. Additionally, it provides important insights to guide efficient real-time appointment scheduling policies in practice.

Thromboembolic Complications in Adult Patients Following Fontan Procedure-A Multicenter Study.

Morbidity and mortality following Fontan (FO) surgery are primarily thromboembolic in nature. However, follow-up data regarding thromboembolic complications (TECs) in adult patients after FO procedure are inconsistent. In this multicenter study, we investigated the incidence of TECs in FO patients. We studied 91 patients who underwent FO procedure. Clinical data, laboratory, and imaging investigations were collected prospectively during the scheduled medical appointments in 3 Adult Congenital Heart Disease Departments in Poland. TECs were recorded during a median follow-up of 31 months. Four patients (4.4%) were lost to follow-up. The mean age of patients was 25.3 (±6.0) years at enrollment, and the mean time between FO operation and investigation was 22.1 (±5.1) years. A total of 21 out of 91 patients (23.1%) had a history of 24 TECs since an FO procedure, mainly pulmonary embolism (PE; n = 12, 13.2%), including 4 (33.3%) silent PE. The mean time since FO operation to the first TEC was 17.8 (±5.1) years. During follow-up, we documented 9 TECs in 7 (8.0%) patients, mainly PE (n = 5, 5.5%). Most patients with TEC had a left type of systemic ventricle (57.1%). Three patients (42.9%) were treated with aspirin, 3 (3.4%) with Vitamin K antagonists or novel oral anticoagulants, and 1 patient had no antithrombotic treatment at the time of TEC occurrence. Supraventricular tachyarrhythmias were present in 3 patients (42.9%). This prospective study shows that TECs are common in FO patients, and a significant number of these events occur during adolescence and young adulthood. We also indicated how much TECs are underestimated in the growing adult FO population. The complexity of the problem requires more studies, especially to standardize the prevention of TECs in the whole FO population.

Care Givers Perceptions Associated With Adherence to Scheduled Medical Appointments Among HIV Infected Children Aged 18 Months to Nine Years Receiving HIV Care Services at Kenyatta National Hospital,Kenya

Care Givers Perceptions Associated With Adherence to Scheduled Medical Appointments Among HIV Infected Children Aged 18 Months to Nine Years Receiving HIV Care Services at Kenyatta National Hospital,Kenya

Nudging Patient Choice: Reducing No-Shows Using Waits Framing Messaging

Healthcare providers have long grappled with patients not showing up for their scheduled medical appointments; such no-shows lead to wasted resources and longer wait times for other patients. However, new operations research offers a promising solution to this problem. The study finds that using text message reminders that include an additional line of text indicating a potentially long wait for the next available appointment can significantly reduce no-shows by a factor of 28.6%. The intervention, called waits framing, was found to be more effective among patients who were more sensitive to wait times and when the information in the message was novel and credible. The study also uncovered the mechanism underlying the intervention. Specifically, the waits framing messages increased the perceived cost of missing an appointment, leading to a reduction in queue abandonment. This study provides insights into how behavioral science can improve service operations and help tackle challenges in healthcare delivery.

The association between health information technology and influenza vaccine uptake among adults with heart disease.

Aim: Assess the association of health information technology (HIT) on influenza vaccine (IV) uptake among adults with heart disease. Methods: The association of four different uses of HIT - (1) look up health information, (2) schedule medical appointments, (3) refillprescriptions online and (4) communicate with healthcare providers on IV uptake -was analyzed using multiple logistic regression. Results: Adults with heart disease who use the internet to look up information (adjusted odds ratio [AOR]=1.48, 95% CI: 1.39-1.59; p<0.001), fill up prescription (AOR=1.87, 95% CI: 1.69-2.06; p<0.001), and communicate with a healthcare provider (AOR=1.95, 95%CI: 1.23-3.10; p=0.005)had a higher uptake of IV unlike those who did not. Conclusion: Using the internet in patient-healthcare system engagement via HIT was associated with increase IV uptake. Future studies should assess the impact of population-level variables on this relationship.

Ethical Redress of Racial Inequities in AI: Lessons from Decoupling Machine Learning from Optimization in Medical Appointment Scheduling

An Artificial Intelligence algorithm trained on data that reflect racial biases may yield racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms used to schedule medical appointments in the USA predict that Black patients are at a higher risk of no-show than non-Black patients, though technically accurate given existing data that prediction results in Black patients being overwhelmingly scheduled in appointment slots that cause longer wait times than non-Black patients. This perpetuates racial inequity, in this case lesser access to medical care. This gives rise to one type of Accuracy-Fairness trade-off: preserve the efficiency offered by using AI to schedule appointments or discard that efficiency in order to avoid perpetuating ethno-racial disparities. Similar trade-offs arise in a range of AI applications including others in medicine, as well as in education, judicial systems, and public security, among others. This article presents a framework for addressing such trade-offs where Machine Learning and Optimization components of the algorithm are decoupled. Applied to medical appointment scheduling, our framework articulates four approaches intervening in different ways on different components of the algorithm. Each yields specific results, in one case preserving accuracy comparable to the current state-of-the-art while eliminating the disparity.

Pre-Visit Planning Calls Conducted by Student Pharmacists at a Student-Run Free Clinic

Background: Student pharmacist participation in Student-Run Free Clinics (SRFCs) has not been well studied. Similarly, there is not much data for the use of pre-visit planning calls in the SRFC setting. Our SRFC is open two days a week and staffed by a multidisciplinary team of medical, nursing, pharmacy, and social work students that are supervised by physicians, nurse practitioners, pharmacists, and clinical social worker preceptors. The objective of our project is to determine the impact of pre-visit telephone calls on the accuracy of existing medication lists in the electronic health record (EHR) and the identification of medication therapy-related problems.&#x0D; Methods: We developed a pre-visit planning call protocol to perform medication reconciliation and identify medication therapy problems (MTPs). Trained student pharmacists called patients prior to their scheduled medical appointment to update their medication list in the EHR and conduct a preliminary assessment of MTPs. We recorded patient demographics, including age, gender, race, co-morbid conditions, medication changes documented in the EHR, and potential MTPs. Descriptive statistics were used to describe the patient population and medication changes. &#x0D; Results: A total of 111 patients participated in 135 patient encounters. Among prescription medications, there were 36 additions, 34 removals, and 97 adjustments to directions. There were 147 nonprescription products added, 34 removed, and 56 adjustments to directions. There were 16 situations in which a patient may have needed additional medication therapy, 14 drug-drug interactions, 12 adverse medication events, and five problems requiring additional monitoring. The median call was 5 minutes in duration. &#x0D; Conclusion: Student pharmacists were able to conduct pre-visit planning telephone calls to improve the accuracy of medication lists and conduct a preliminary assessment of MTPs prior to patient visits at the SRFC.

Chronic obstructive pulmonary disease exacerbations' management in Portuguese hospitals – EvaluateCOPDpt, a multicentre, observational, prospective study

Introduction and ObjectivesIn order to improve the quality of chronic obstructive pulmonary disease (COPD) patients' care, better knowledge of clinical practice and the factors associated with patient outcomes are needed. This study aimed to evaluate the relation between clinical practice and the outcomes of patients admitted for COPD exacerbations in Portuguese hospitals. Materials and MethodsObservational, multicentre, prospective study with a 60-days follow-up period, in 11 hospitals, including patients aged ≥ 30 years, admitted to hospital for at least 24 hours due to an acute exacerbation of COPD. Demographic and clinical data were collected, including sex, age, smoking habits, hospitalisations, pulmonary function, comorbidities, COPD symptoms, and treatment. Sixty days after discharge, COPD exacerbations management, outcome measures, and readmission data were evaluated through a structured phone follow-up interview. Results196 patients were included (85.7% male, mean age 71.2 years), the majority admitted through the emergency service. Ex-smokers and current smokers accounted for 51% and 36%, respectively. On admission, 72.4% were on LAMA, 54.6% on LABA, and 45.5% were on LABA/LAMA. Inhaled corticosteroids (ICS) were used in 37.3% and systemic steroids (SCS) in 10.3%. 35.7 % had had at least one exacerbation, with hospitalisation, in the previous year. There was no spirometry data for 23.2%. On hospitalisation, 98.5% of patients were treated with oxygen and 38.3% with non-invasive ventilation. Additionally, 93.4% used SCS and 60.2% ICS. Antibiotics were administered to 85.2%. 95.4% of patients were discharged; 9 died, 5 of whom had a COPD-related death. The median length of stay was 12 days for discharged patients and 33 days for patients who died. At discharge, 79.1% were prescribed with LAMA, 63.6% SCS, 61.5% LABA and 55.6% LAMA+LABA. 26,2% were prescribed with ICS+LABA+LAMA. At follow-up, 44.4% had a scheduled medical appointment within the 60 days after being discharged, and 28.3% were later readmitted due to exacerbation, of whom 52.8% were hospitalised. ConclusionsThe severity of COPD, particularly in exacerbations, is directly related to impaired lung function and quality of life, mortality, and significant health system costs. Knowledge about COPD exacerbations' management in acute hospital admissions in Portugal may help stimulate a national discussion and review of existing data to engage clinicians, policymakers, managers, and patients, raising awareness and promoting action on COPD.

Machine learning approaches to predicting no-shows in pediatric medical appointment

Patients’ no-shows, scheduled but unattended medical appointments, have a direct negative impact on patients’ health, due to discontinuity of treatment and late presentation to care. They also lead to inefficient use of medical resources in hospitals and clinics. The ability to predict a likely no-show in advance could enable the design and implementation of interventions to reduce the risk of it happening, thus improving patients’ care and clinical resource allocation. In this study, we develop a new interpretable deep learning-based approach for predicting the risk of no-shows at the time when a medical appointment is first scheduled. The retrospective study was conducted in an academic pediatric teaching hospital with a 20% no-show rate. Our approach tackles several challenges in the design of a predictive model by (1) adopting a data imputation method for patients with missing information in their records (77% of the population), (2) exploiting local weather information to improve predictive accuracy, and (3) developing an interpretable approach that explains how a prediction is made for each individual patient. Our proposed neural network-based and logistic regression-based methods outperformed persistence baselines. In an unobserved set of patients, our method correctly identified 83% of no-shows at the time of scheduling and led to a false alert rate less than 17%. Our method is capable of producing meaningful predictions even when some information in a patient’s records is missing. We find that patients’ past no-show record is the strongest predictor. Finally, we discuss several potential interventions to reduce no-shows, such as scheduling appointments of high-risk patients at off-peak times, which can serve as starting point for further studies on no-show interventions.

Application of the experimental design for the analysis of nonattendance to scheduled medical appointments in the Colombian Health System

This paper shows a study about nonattendance to medical appointments in the Colombian health system to determine which strategies have the greatest effect on the response variable (nonattendance) of scheduled medical appointments. Three factors were considered (calling the patient, reconfirming on the eve of the appointment, confirming understanding of the procedure), each with two or three levels randomly selected from a population. Two replicates were taken for each combination of treatments. The experimental runs were donerandomly to guarantee the absence of bias in data collection. With the above, a total of 180 experimental runs were carried out. This study aims to determine the factors that influence the population when they do not attend their medical appointments to propose a strategy that reduces this phenomenon. A design of experiments (DoE) was performed, specifically a completely randomized design (CRD) and a 3k factorial design

A Smartphone Based Application for Medical Assistance of Elderly Patients

The importance of a routine health check-up for elderly patients, as well as the documentation of that check- up, cannot be overstated. Maintaining adequate documentation for a patient by himself or by his attendance, on the other hand, is extremely difficult. This paper primarily focuses on developing a framework that includes a smartphone application and introduces a real-time remote monitoring system for basic medical treatment of an elderly patient. It can keep track of a patient’s emergency contacts. Using the SMS manager API, emergency contacts can receive messages with the patient’s daily health alerts. Furthermore, the system will keep track of the patient’s symptoms, and if anything goes wrong for a continuous period of time, the system will automatically send out reminders to schedule medical appointments. For medical appointments, a patient may be able to search for nearest doctors and schedule an appointment for him via the system. Here, Google map technology is used to search nearby doctors. A reminder is introduced in this system where all of the activities for a patient are notified on a regular basis.

Technology Use Among Older Adults to Manage their Health During a Global Pandemic

Risk of severe COVID-19 illness increases with age, and older adults are more likely to be hospitalized and die from COVID-19 and related complications as compared to their younger counterparts. This reality, combined with pandemic-related lockdown and social distancing policies, has increased in-home isolation for older adults. This includes cancelling in-person healthcare appointments and conducting many appointments via tele-health. As older adults have had to quickly pivot to learning new technologies, little is known about their experiences with navigating virtual healthcare during the pandemic. Therefore, this qualitative study aims to address that gap. One-on-one interviews (N=29) were conducted with older adults (Mean age=71.5; 86% female) via phone/Zoom. Participants were asked about their healthcare experiences during the pandemic and the role technology played. Interviews were transcribed and thematically analyzed using Nvivo12 software. Findings demonstrate that participants used technology to schedule medical appointments, engage in virtual visits with their providers, set reminders to take medications, and undertake their daily exercise routine. Post-lockdown, some participants preferred in-person visits due to the nature of their diagnosis, personal preference, or unfamiliarity with the needed technology. Older adults encountered challenges including cancelled appointments, miscommunication with providers, and lack of skill to use technologies. Cancellation of appointments and postponement of treatments affected the health of some of the participants. Implications of this research can inform tele-health approaches with older patients, as well as provider communication and coordination of care. Leveraging technology for preventative health approaches can also assist older adults in ongoing health maintenance and promote well-being.

Chatbot with Facemask Detection Technique

Artificial intelligence is now in full swing, and chatbots are just a blip on the radar of a massive wave of advancement. Virtual chatbots that simulate human discussions for performing various tasks are becoming increasingly popular as messengers become more widely used. Some chatbots can schedule medical appointments, order a taxi, transfer money to pals, check into a flight, and perform a variety of other functions. Chatbots assist consumers as well, and they are becoming increasingly interested in this technology. Given the current COVID-19 situation, it's critical to be informed about the growing number of cases and the many precautions that must be followed. This information will be obtained with the assistance of the chatbot. Our chatbot not only converses with users, but it also informs them about Covid-19 and the measures that must be taken. We also understand the importance of wearing masks to prevent the infection from spreading. Using image processing, we will determine if people are wearing their masks or not in this section of our research. To determine if people are wearing a face mask or not, we will develop this section of the project using a simple and basic Convolutional Neural Network (CNN) model, TensorFlow with Keras library, and OpenCV. In this project, we will use a dataset that contains two types of images: images with masks and images without masks. Then, using the webcam on your PC, we'll utilize these images to create a CNN model using TensorFlow to detect if you're wearing a face mask.

The quandary of adolescent HIV: Perspectives of key informants in Accra, Ghana

This study explored key informants’ perspectives about adolescents living with human immunodeficiency virus (HIV). Twenty-one participants were purposively selected and data were collected through face-to-face in-depth interviews using a semi-structured interview guide. The interviews were audio-recorded, transcribed, and analyzed to identify emerging themes. The findings indicated that adolescents living with HIV had concerns about disclosing their HIV status. Key informants reported that adolescents living with HIV’s responses to awareness about their status included anger, blame and social isolation. Also, it was found that the adolescents had challenges with adhering to scheduled medical appointments and the antiretroviral therapy. Evidence showed that financial constraints adversely affected adolescents’ clinic attendance and medication compliance. Several psychosocial support services (counseling, financial assistance, provision of basic needs, and cordial relationships) were identified by key informants as being available for adolescents living with HIV. Based on the findings of the study, conclusions were drawn and implications discussed.

The Impact of the SARS-CoV-2 Outbreak on the Psychological Flexibility and Behaviour of Cancelling Medical Appointments of Italian Patients with Pre-Existing Medical Condition: The "ImpACT-COVID-19 for Patients" Multi-Centre Observational Study.

Psychological distress imposed by the SARS-CoV-2 outbreak particularly affects patients with pre-existing medical conditions, and the progression of their diseases. Patients who fail to keep scheduled medical appointments experience a negative impact on care. The aim of this study is to investigate the psychosocial factors contributing to the cancellation of medical appointments during the pandemic by patients with pre-existing health conditions. Data were collected in eleven Italian hospitals during the last week of lockdown, and one month later. In order to assess the emotional impact of the SARS-CoV-2 outbreak and the subject’s degree of psychological flexibility, we developed an ad hoc questionnaire (ImpACT), referring to the Acceptance and Commitment Therapy (ACT) model. The Impact of Event Scale-Revised (IES-R), the Depression, Anxiety and Stress Scale (DASS) and the Cognitive Fusion Questionnaire (CFQ) were also used. Pervasive dysfunctional use of experiential avoidance behaviours (used with the function to avoid thought, emotions, sensations), feelings of loneliness and high post-traumatic stress scores were found to correlate with the fear of COVID-19, increasing the likelihood of cancelling medical appointments. Responding promptly to the information and psychological needs of patients who cancel medical appointments can have positive effects in terms of psychological and physical health.

Cumulative Effects of Stigma Experiences on Retention in HIV Care Among Men and Women in the Rural Southeastern United States.

The stigmatization of HIV infection impedes every step along the HIV continuum of care, particularly care engagement and retention. The differential effects of various sources of stigma on retention in HIV care have been the subject of limited research. We examined the accumulation of HIV stigma experiences over 1 year in relationship to treatment retention among 251 men and women marginally engaged in HIV care in the southeastern United States. Results showed that cumulative stigma experiences predicted poorer retention in care, with greater stigma experiences related to less consistency in attending scheduled medical appointments. HIV stigma originating from family members and acts of overt discrimination were the most frequently experienced sources of stigma and were most closely associated with disengagement from HIV care. In addition, analyses by gender indicated that retention in care for men was impacted by stigma to a greater extent than among women. These findings reaffirm the importance of HIV stigma as a barrier to HIV care and provide new directions for interventions to mitigate the negative effects of stigma on patients who are not fully engaged in HIV care. Clinical trials registration NCT104180280.

Prioritization of Patients with Abnormal Breast Findings in theAlerta RosaNavigation Program to Reduce Diagnostic Delays

In Mexico, there are considerable health system delays in the diagnosis and treatment initiation of women with breast cancer. Alerta Rosa is a navigation program in Nuevo Leon that aims to reduce barriers that impede the timely management of these patients. Since December 2017, women who registered to receive medical evaluations by Alerta Rosa were stratified based on their clinical characteristics into three priority groups ("Red," "Yellow," and "Green"). According to the category assigned, patients were scheduled imaging studies and medical appointments with breast specialists on a preferential basis. Up until December 2019, 561 patients were scheduled for medical evaluations. Of them, 59% were classified as "Red," 25% "Yellow," and 16% "Green" priority. The median time from stratification to first medical evaluation was 4, 6, and 7 days, respectively (p = .003). Excluding those who had a prior breast cancer diagnosis, 21 patients were diagnosed by Alerta Rosa, with the initial "Red" priority classification demonstrating a sensitivity of 95% (95% confidence interval [CI], 75.1%-99.9%) and specificity of 42% (95% CI, 37.1%-47.1%) for breast cancer. The median time elapsed from initial patient contact to diagnosis and treatment initiation was 16 days and 39 days, respectively. The majority (72%) of patients were diagnosed at an early stage (0-II). This patient prioritization system adequately identified women with different probabilities of having breast cancer. Efforts to replicate similar triage systems in resource-constrained settings where screening programs are ineffective could prove to be beneficial in reducing diagnostic intervals and achieving early-stage diagnoses. Low- and middle-income countries such as Mexico currently lack the infrastructure to achieve effective breast cancer screening and guarantee prompt access to health care when required. To reduce the disease burden in such settings, strategies targeting early detection are urgently needed. Patient navigation programs aid in the reduction of health system intervals and optimize the use of available resources. This article presents the introduction of a triage system based on initial patient concern. Appointment prioritization proved to be successful at reducing health system intervals and achieving early-stage diagnoses by overcoming barriers that impede early access to quality medical care.

Impact of the COVID-19 pandemic in children with allergic asthma

Impact of the COVID-19 pandemic in children with allergic asthma