Implementation and Application of Artificial Intelligence in Selected Public Services

Data-intensive technologies, such as artificial intelligence, imply huge opportunities for transforming the delivery of healthcare and social services, improving people’s quality of life and working in the health and welfare system. The aim of this paper is to present examples of the implementation of artificial intelligence techniques in healthcare and social services and to sketch the trends and challenges in the adoption of artificial intelligence techniques, with an emphasis on the public sector and selected public services. Analysis is based on a realistic assessment of current artificial intelligence technologies and their anticipated development. Besides the benefits and potential opportunities for healthcare and social services, there are also challenges for governments. Understanding the huge potential of artificial intelligence as well as its limitations will be a key step forward, but it is essential to avoid the trap of an overestimation of artificial intelligence potential

The Impact of Probabilistic Classifiers on Appointment Scheduling with No-Shows

Appointment no-shows are common in outpatient clinics and increase clinic costs and patients’ dissatisfaction. We develop a framework to predict the no-show probabilities of a given set of patients, and to subsequently employ these predictions to find the optimal appointment schedule. Some existing work assumes that all patients have the same no-show probability (1-class approach); other work assumes that patients have either a low or a high no-show probability (2-class approach). In contrast, we utilize probabilistic classifiers to obtain the individual patients’ no-show probabilities (N-class approach). Our approach results in better-quality schedules, as measured by a weighted average of patient waiting time and provider overtime. We also find that a small increase in the prediction performance (measured by the Brier score) translates into a large decrease in the schedule cost. Our results are obtained through a large-scale computational study and validated on a real-world data set from an outpatient clinic

Predicting Online Invitation Responses with a Competing Risk Model Using Privacy-Friendly Social Event Data

Predicting people's responses to invitations is an important issue for social event management, as the decision-making process behind member responses to invitations is complicated. The purpose of this paper is to suggest a privacy-friendly method to predict whether and when people will respond to open invitations. We apply the competing risk model to predict member responses. The predictive model uses past social event participation data to infer a network structure among people who accept or reject invitations. The inferred networks collectively show the extent to which people are likely to accept or reject invitations. Validated using real datasets including 31,230 people and 8,885 events, the proposed method not only presents the variables that predict attendance (such as past attendance and social network), but also those that predict faster responses. This approach is privacy friendly, as it requires no personal information regarding people and social events (such as name, age and gender or event content). This work contributes to the predictive modeling literature as the first study of a competing risk model developed for replies to a social invitation. Our findings will help event organizers predict how many people will attend events, allowing them to organize effectively

Predicting scheduled hospital attendance with artificial intelligence

Failure to attend scheduled hospital appointments disrupts clinical management and consumes resource estimated at £1 billion annually in the United Kingdom National Health Service alone. Accurate stratification of absence risk can maximize the yield of preventative interventions. The wide multiplicity of potential causes, and the poor performance of systems based on simple, linear, low-dimensional models, suggests complex predictive models of attendance are needed. Here, we quantify the effect of using complex, non-linear, high-dimensional models enabled by machine learning. Models systematically varying in complexity based on logistic regression, support vector machines, random forests, AdaBoost, or gradient boosting machines were trained and evaluated on an unselected set of 22,318 consecutive scheduled magnetic resonance imaging appointments at two UCL hospitals. Highdimensional Gradient Boosting Machine-based models achieved the best performance reported in the literature, exhibiting an area under the receiver operating characteristic curve of 0.852 and average precision of 0.511. Optimal predictive performance required 81 variables. Simulations showed net potential benefit across a wide range of attendance characteristics, peaking at £3.15 per appointment at current prevalence and call efficiency. Optimal attendance prediction requires more complex models than have hitherto been applied in the field, reflecting the complex interplay of patient, environmental, and operational causal factors. Far from an exotic luxury, high-dimensional models based on machine learning are likely essential to optimal scheduling amongst other operational aspects of hospital care. High predictive performance is achievable with data from a single institution, obviating the need for aggregating large-scale sensitive data across governance boundaries

PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data

With calls for increasing transparency, governments are releasing greater amounts of data in multiple domains including finance, education and healthcare. The efficient exploratory analysis of healthcare data constitutes a significant challenge. Key concerns in public health include the quick identification and analysis of trends, and the detection of outliers. This allows policies to be rapidly adapted to changing circumstances. We present an efficient outlier detection technique, termed PIKS (Pruned iterative-k means searchlight), which combines an iterative k-means algorithm with a pruned searchlight based scan. We apply this technique to identify outliers in two publicly available healthcare datasets from the New York Statewide Planning and Research Cooperative System, and California's Office of Statewide Health Planning and Development. We provide a comparison of our technique with three other existing outlier detection techniques, consisting of auto-encoders, isolation forests and feature bagging. We identified outliers in conditions including suicide rates, immunity disorders, social admissions, cardiomyopathies, and pregnancy in the third trimester. We demonstrate that the PIKS technique produces results consistent with other techniques such as the auto-encoder. However, the auto-encoder needs to be trained, which requires several parameters to be tuned. In comparison, the PIKS technique has far fewer parameters to tune. This makes it advantageous for fast, "out-of-the-box" data exploration. The PIKS technique is scalable and can readily ingest new datasets. Hence, it can provide valuable, up-to-date insights to citizens, patients and policy-makers. We have made our code open source, and with the availability of open data, other researchers can easily reproduce and extend our work. This will help promote a deeper understanding of healthcare policies and public health issues

Medical workflow design and planning using Node-Red data fusion

The space of clinical planning requires a complex arrangement of information, often not capable of being captured in a singular dataset. As a result, data fusion techniques can be used to combine multiple data sources as a method of enriching data to mimic and compliment the nature of clinical planning. These techniques are capable of aiding healthcare providers to produce higher quality clinical plans and better progression monitoring techniques. Clinical planning and monitoring are important facets of healthcare which are essential to improving the prognosis and quality of life of patients with chronic and debilitating conditions such as COPD. To exemplify this concept, we utilize a Node-Red-based clinical planning and monitoring too that combines data fusion techniques using the JDL Model for data fusion and a domain specific language which features a self-organizing abstract syntax tree

Strategies for Implementing a Successful Customer Relationship Management System

Customer relationship management (CRM) software implementation fails in thepackaging industry because of ineffective CRM implementation strategies. Effective strategies for CRM software implementation are essential to CRM managers for improving CRM project success rates. Grounded in Kano’s customer satisfaction theory, the purpose of this qualitative multiple case study was to explore the strategies CRM managers in the packaging industry used to operate a profitable business. Data were collected through semi-structured interviews and documents of CRM strategies. Participants were four CRM managers located in Illinois who had a minimum of 10 years of successfully managing CRM systems with high success rates. Data analysis was done through thematic analysis. Data analysis revealed three themes: top management commitment, technical capability, and implementation teams. The key recommendation is for CRM managers to involve key stakeholders during the earliest stage of the project. Implications for positive social change include the potential to improve product quality by eliminating waste and reducing pollution’s environmental effects through automated processes to eliminate task repetition and streamline the lead process

Strategies for Implementing a Successful Customer Relationship Management System

Customer relationship management (CRM) software implementation fails in thepackaging industry because of ineffective CRM implementation strategies. Effective strategies for CRM software implementation are essential to CRM managers for improving CRM project success rates. Grounded in Kano’s customer satisfaction theory, the purpose of this qualitative multiple case study was to explore the strategies CRM managers in the packaging industry used to operate a profitable business. Data were collected through semi-structured interviews and documents of CRM strategies. Participants were four CRM managers located in Illinois who had a minimum of 10 years of successfully managing CRM systems with high success rates. Data analysis was done through thematic analysis. Data analysis revealed three themes: top management commitment, technical capability, and implementation teams. The key recommendation is for CRM managers to involve key stakeholders during the earliest stage of the project. Implications for positive social change include the potential to improve product quality by eliminating waste and reducing pollution’s environmental effects through automated processes to eliminate task repetition and streamline the lead process

An Industrial Engineering-Based Approach to Designing and Evaluating Healthcare Systems to Improve Veteran Access to Care

Access to healthcare is a critical public health issue in the United States, especially for veterans. Veterans are older on average than the general U.S. population and are thus at higher risk for chronic disease. Further, veterans report more delays when seeking healthcare. The Veterans Affairs (VA) Healthcare System continuously works to develop policies and technologies that aim to improve veteran access to care. Industrial engineering methods can be effective in analyzing the impact of such policies, as well as designing or modifying systems to better align veteran patients’ needs with providers and resources. This dissertation demonstrates how industrial engineering tools can guide policy decisions to improve healthcare access by connecting veterans with the most appropriate healthcare resources, while highlighting the trade-offs inherent in such decisions. This work comprises four stages: (1) using optimization methods to design a healthcare network when introducing new provider options for chronic disease screening, (2) developing simulation tools to model how access to care is impacted when scheduling policies accommodate patient preferences, and (3) simulating triage strategies for non-emergency care during COVID-19, and (4) evaluating how treatment decisions impact patient access when guided by risk-based prediction models compared to current practice. In the first stage, we consider veteran access to chronic eye disease screening. Ophthalmologists in the VA have developed a platform in which ophthalmic technicians screen patients for major chronic eye diseases during primary care visits. We use mixed-integer programming-based facility location models to understand how the VA can determine which clinics should offer eye screenings, which provider type(s) should staff those clinics, and how to distribute patients among clinics. The results of this work show how the VA can achieve various objectives including minimizing the cost or maximizing the number of patients receiving care. In the second stage, we simulate patients seeking care for gastroesophageal reflux disease with primary care and gastrointestinal providers. This simulation incorporates policies about how to schedule patients for visits in various modalities, including face-to-face and telehealth, and also considers uncertainty in key factors like patient arrivals and demographics. Results of these models can help us understand how scheduling based on these preferences impacts access, including time to first appointment and number of patients seen. Such metrics can guide healthcare administrators as new technologies are introduced that offer options for how patients interact with their providers. In the third stage, we simulate patients seeking non-emergency outpatient care under reduced appointment capacity due to the COVID-19 pandemic. We demonstrate this using endoscopy visits as a central example. We use our simulation model to understand how various strategies for adjusting patient triage and/or clinic operations can mitigate patient backlog and reduce patient waiting times. In the fourth stage, we integrate multiple industrial engineering methods to examine how access is impacted among chronic liver disease patients when predictive modeling is introduced into treatment planning. We developed a simulation model to help clinical decision-makers better understand how using a predictive model may change the care pathway for a specific patient and also impact system decisions, such as required staffing levels and clinical data acquired at specific patient visits. The model also helps clinicians understand the value of specific clinical data (lab values, vitals, etc.) by demonstrating how better or worse inputs to the predictive models have larger system impacts to patient access.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169942/1/ajvandeu_1.pd

Analysis of HbA1c, Medication Compliance, Income Subsidies, and Comorbidity in Medicare Type 2 Diabetics

Diabetes is one of America’s leading chronic diseases with comorbidities contributing to lower health statuses and increased health care costs. While it is known that lowering HbA1c reduces the deleterious effects of diabetes, the capability to identify people with diabetes at risk for uncontrolled HbA1c levels or developing comorbidities based on the compliance rates for different oral antihyperglycemic medication classes (OAMCs) and financial assistance programs does not yet exist. These quantitative longitudinal retrospective studies examined the association between medication compliance, using Proportion of Days Covered (PDC), by OAMC and Medicare financial aid programs, on predicting HbA1c levels and comorbidities in type 2 diabetics. Jaam’s medication compliance framework guided sample selection from the 2019 claims database of a large Managed Care Organization with limited eligibility of only 60% of the population which had an HbA1c level checked in the past 12 months. Multiple regression analyses revealed that as compliance rates improve, different OAMC combinations are associated with significant and variable reductions in A1c levels but with minimal effect strengths not allowing the linear regression model to be used as a predictive tool. Financial assistance programs have a small, but statistically significant effect on reducing HbA1c levels, comorbidities, or improving compliance rates. These studies are the first to investigate the association between PDC compliance rates for OAMCs on HbA1c and comorbidities. These findings contribute to positive social change by demonstrating that variable patient compliance rates for different OAMC medication classes and HbA1c testing should be considered when prescribing diabetic therapeutic regimens to achieve optimal HbA1c control and improved health status