Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review

BACKGROUND

There is an emerging need for evidence-based approaches harnessing large amounts of health care data and novel technologies (such as artificial intelligence) to optimize public health policy making.

OBJECTIVE

The aim of this review was to explore the data analytics tools designed specifically for policy making in noncommunicable diseases (NCDs) and their implementation.

METHODS

A scoping review was conducted after searching the PubMed and IEEE databases for articles published in the last 10 years.

RESULTS

Nine articles that presented 7 data analytics tools designed to inform policy making for NCDs were reviewed. The tools incorporated descriptive and predictive analytics. Some tools were designed to include recommendations for decision support, but no pilot studies applying prescriptive analytics have been published. The tools were piloted with various conditions, with cancer being the least studied condition. Implementation of the tools included use cases, pilots, or evaluation workshops that involved policy makers. However, our findings demonstrate very limited real-world use of analytics by policy makers, which is in line with previous studies.

CONCLUSIONS

Despite the availability of tools designed for different purposes and conditions, data analytics is not widely used to support policy making for NCDs. However, the review demonstrates the value and potential use of data analytics to support policy making. Based on the findings, we make suggestions for researchers developing digital tools to support public health policy making. The findings will also serve as input for the European Union-funded research project ONCODIR developing a policy analytics dashboard for the prevention of colorectal cancer as part of an integrated platform.

Factors impacting outcomes of cochlear implantation in children at two University centres in China: Multi-year analysis from the Paediatric Implanted Recipient Observational Study (P-IROS)

OBJECTIVES

To identify factors affecting functional hearing performance and quality of life (QoL) outcomes in paediatric cochlear implantation (CI) recipients at two University centres in mainland China.

METHODS

Two university centres in mainland China, part of the prospective longitudinal Paediatric Implanted Recipient Observational Study (P-IROS), contributed participant data. Participants were aged under 10 years at time of CI. Functional hearing performance and QoL measures were collected prior to device activation, and at 6-monthly intervals for 2 years post-implantation. Functional hearing endpoints including Categories of Auditory Performance-II (CAP-II) and QoL were evaluated and analysed using ordinal mixed-effects regression models.

RESULTS

Data were from 288 children with a mean age at implant of 2.74 years. Overall follow-up at 1 year was 59% and 51% at 2 years. Younger age at implantation (p<0.001) and hearing aid use preimplantation (p=0.026) were associated with significant benefit. Bilateral device users (both CI and bimodal) achieved significantly better functional hearing performance on the CAP-II than unilateral CI users (p<0.001). Slower functional hearing improvements were observed in those with lower parental expectations compared to higher expectations (p<0.001). QoL improved over time but followed a different initial trajectory between centres.

CONCLUSION

All participants demonstrated significant improvements in auditory performance and QoL over time. Younger age at CI, and bilateral/bimodal device fitting contributed to earlier improvements. Other potential factors that could help inform families, professionals, and health authorities about choice of hearing device and educational supports required included aetiology of hearing loss and level of maternal education.

The application of artificial intelligence in health policy: a scoping review

BACKGROUND

Policymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking.

METHODS

We searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords "artificial intelligence" and "policymaking." We used Walt and Gilson's policy triangle framework for charting the data.

RESULTS

The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts.

CONCLUSION

Numerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI's potential to foster the meaningful use of evidence-based policymaking.

Big data-driven public health policy making: Potential for the healthcare industry

The use of healthcare data analytics is anticipated to play a significant role in future public health policy formulation. Therefore, this study examines how big data analytics (BDA) may be methodically incorporated into various phases of the health policy cycle for fact-based and precise health policy decision-making. So, this study explores the potential of BDA for accurate and rapid policy-making processes in the healthcare industry. A systematic review of literature spanning 22 years (from January 2001 to January 2023) has been conducted using the PRISMA approach to develop a conceptual framework. The study introduces the emerging topic of BDA in healthcare policy, goes over the advantages, presents a framework, advances instances from the literature, reveals difficulties and provides recommendations. This study argues that BDA has the ability to transform the conventional policy-making process into data-driven process, which helps to make accurate health policy decision. In addition, this study contends that BDA is applicable to the different stages of health policy cycle, namely policy identification, agenda setting as well as policy formulation, implementation and evaluation. Currently, descriptive, predictive and prescriptive analytics are used for public health policy decisions on data obtained from several common health-related big data sources like electronic health reports, public health records, patient and clinical data, and government and social networking sites. To effectively utilize all of the data, it is necessary to overcome the computational, algorithmic and technological obstacles that define today's extremely heterogeneous data landscape, as well as a variety of legal, normative, governance and policy limitations. Big data can only fulfill its full potential if data are made available and shared. This enables public health institutions and policymakers to evaluate the impact and risk of policy changes at the population level.

Remote Technologies to Enhance Service Delivery for Adults: Clinical Research Perspectives

There are many examples of remote technologies that are clinically effective and provide numerous benefits to adults with hearing loss. Despite this, the uptake of remote technologies for hearing healthcare has been both low and slow until the onset of the COVID-19 pandemic, which has been a key driver for change globally. The time is now right to take advantage of the many benefits that remote technologies offer, through clinical, consumer, or hybrid services and channels. These include greater access and choice, better interactivity and engagement, and tailoring of technologies to individual needs, leading to clients who are better informed, enabled, and empowered to self-manage their hearing loss. This article provides an overview of the clinical research evidence-base across a range of remote technologies along the hearing health journey. This includes qualitative, as well as quantitative, methods to ensure the end-users' voice is at the core of the research, thereby promoting person-centered principles. Most of these remote technologies are available and some are already in use, albeit not widespread. Finally, whenever new technologies or processes are implemented into services, be they clinical, hybrid, or consumer, careful consideration needs to be given to the required behavior change of the key people (e.g., clients and service providers) to facilitate and optimize implementation.

Profiling hearing aid users through big data explainable artificial intelligence techniques

Debilitating hearing loss (HL) affects ~6% of the human population. Only 20% of the people in need of a hearing assistive device will eventually seek and acquire one. The number of people that are satisfied with their Hearing Aids (HAids) and continue using them in the long term is even lower. Understanding the personal, behavioral, environmental, or other factors that correlate with the optimal HAid fitting and with users' experience of HAids is a significant step in improving patient satisfaction and quality of life, while reducing societal and financial burden. In SMART BEAR we are addressing this need by making use of the capacity of modern HAids to provide dynamic logging of their operation and by combining this information with a big amount of information about the medical, environmental, and social context of each HAid user. We are studying hearing rehabilitation through a 12-month continuous monitoring of HL patients, collecting data, such as participants' demographics, audiometric and medical data, their cognitive and mental status, their habits, and preferences, through a set of medical devices and wearables, as well as through face-to-face and remote clinical assessments and fitting/fine-tuning sessions. Descriptive, AI-based analysis and assessment of the relationships between heterogeneous data and HL-related parameters will help clinical researchers to better understand the overall health profiles of HL patients, and to identify patterns or relations that may be proven essential for future clinical trials. In addition, the future state and behavioral (e.g., HAids Satisfiability and HAids usage) of the patients will be predicted with time-dependent machine learning models to assist the clinical researchers to decide on the nature of the interventions. Explainable Artificial Intelligence (XAI) techniques will be leveraged to better understand the factors that play a significant role in the success of a hearing rehabilitation program, constructing patient profiles. This paper is a conceptual one aiming to describe the upcoming data collection process and proposed framework for providing a comprehensive profile for patients with HL in the context of EU-funded SMART BEAR project. Such patient profiles can be invaluable in HL treatment as they can help to identify the characteristics making patients more prone to drop out and stop using their HAids, using their HAids sufficiently long during the day, and being more satisfied by their HAids experience. They can also help decrease the number of needed remote sessions with their Audiologist for counseling, and/or HAids fine tuning, or the number of manual changes of HAids program (as indication of poor sound quality and bad adaptation of HAids configuration to patients' real needs and daily challenges), leading to reduced healthcare cost.

A Modelling Framework for Evidence-based Public Health Policy Making

It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision- making tools tailored to it. This is because the management of health conditions and their consequences at a public health policy making level can benefit from such type of analysis of heterogeneous data, including health care devices usage, physiological, cognitive, clinical and medication, personal, behavioural, lifestyle data, occupational and environmental data. In this paper we present a novel approach to public health policy making in a form of an ontology, and an integrated platform for realising this approach. Our solution is model-driven and makes use of big data analytics technology. More specifically, it is based on public health policy decision making (PHPDM) models that steer the public health policy decision making process by defining the data that need to be collected, the ways in which they should be analysed in order to produce the evidence useful for public health policymaking, how this evidence may support or contradict various policy interventions (actions), and the stakeholders involved in the decision-making process. The resulted web-based platform has been implemented using Hadoop, Spark and HBASE, developed in the context of a research programme on public health policy making for the management of hearing loss called EVOTION, funded by the Horizon 2020.

Real-World Hearing Aid Usage Patterns and Smartphone Connectivity

Data for monitoring individual hearing aid usage has historically been limited to retrospective questionnaires or data logged intrinsically in the hearing aid cumulatively over time (e. g., days or more). This limits the investigation of longitudinal interactions between hearing aid use and environmental or behavioral factors. Recently it has become possible to analyze remotely logged hearing aid data from in-market and smartphone compatible hearing aids. This can provide access to novel insights about individual hearing aid usage patterns and their association to environmental factors. Here, we use remotely logged longitudinal data from 64 hearing aid users to establish basic norms regarding smartphone connectivity (i.e., comparing remotely logged data with cumulative true hearing aid on-time) and to assess whether such data can provide representative information about ecological usage patterns. The remotely logged data consists of minute-by-minute timestamped logs of cumulative hearing aid on-time and characteristics of the momentary acoustic environment. Using K-means clustering, we demonstrate that hourly hearing aid usage patterns (i.e., usage as minutes/hour) across participants are separated by four clusters that account for almost 50% of the day-to-day variation. The clusters indicate that hearing aids are worn either sparsely throughout the day; early morning to afternoon; from noon to late evening; or across the day from morning to late evening. Using linear mixed-effects regression modeling, we document significant associations between daily signal-to-noise, sound intensity, and sound diversity with hearing aid usage. Participants encounter louder, noisier, and more diverse sound environments the longer the hearing aids are worn. Finally, we find that remote logging via smartphones underestimates the daily hearing aid usage with a pooled median of 1.25 h, suggesting an overall connectivity of 85%. The 1.25 h difference is constant across days varying in total hearing aid on-time, and across participants varying in average daily hearing aid-on-time, and it does not depend on the identified patterns of daily hearing aid usage. In sum, remote data logging with hearing aids has high representativeness and face-validity, and can offer ecologically true information about individual usage patterns and the interaction between usage and everyday contexts.

Can Haptic Stimulation Enhance Music Perception in Hearing-Impaired Listeners?

Cochlear implants (CIs) have been remarkably successful at restoring hearing in severely-to-profoundly hearing-impaired individuals. However, users often struggle to deconstruct complex auditory scenes with multiple simultaneous sounds, which can result in reduced music enjoyment and impaired speech understanding in background noise. Hearing aid users often have similar issues, though these are typically less acute. Several recent studies have shown that haptic stimulation can enhance CI listening by giving access to sound features that are poorly transmitted through the electrical CI signal. This “electro-haptic stimulation” improves melody recognition and pitch discrimination, as well as speech-in-noise performance and sound localization. The success of this approach suggests it could also enhance auditory perception in hearing-aid users and other hearing-impaired listeners. This review focuses on the use of haptic stimulation to enhance music perception in hearing-impaired listeners. Music is prevalent throughout everyday life, being critical to media such as film and video games, and often being central to events such as weddings and funerals. It represents the biggest challenge for signal processing, as it is typically an extremely complex acoustic signal, containing multiple simultaneous harmonic and inharmonic sounds. Signal-processing approaches developed for enhancing music perception could therefore have significant utility for other key issues faced by hearing-impaired listeners, such as understanding speech in noisy environments. This review first discusses the limits of music perception in hearing-impaired listeners and the limits of the tactile system. It then discusses the evidence around integration of audio and haptic stimulation in the brain. Next, the features, suitability, and success of current haptic devices for enhancing music perception are reviewed, as well as the signal-processing approaches that could be deployed in future haptic devices. Finally, the cutting-edge technologies that could be exploited for enhancing music perception with haptics are discussed. These include the latest micro motor and driver technology, low-power wireless technology, machine learning, big data, and cloud computing. New approaches for enhancing music perception in hearing-impaired listeners could substantially improve quality of life. Furthermore, effective haptic techniques for providing complex sound information could offer a non-invasive, affordable means for enhancing listening more broadly in hearing-impaired individuals.

Electronic Health Records As a Platform for Audiological Research: Data Validity, Patient Characteristics, and Hearing-Aid Use Persistence Among 731,213 U.S. Veterans

OBJECTIVES

This article presents a summary of audiological, general health, and hearing aid (HA) outcome data in a large sample of U.S. Veterans receiving HAs. The current article also provides the foundation for a series of papers that will explore relationships between a wide range of factors and HA outcomes.

DESIGN

The patient sample is all (n = 731,213) patients for whom HAs were ordered between April 2012 and October 2014 through the U.S. Veterans Health Administration Remote Order Entry System. For these patients, Veterans Affairs electronic health records (EHRs) stored in various databases provided data on demographics, received diagnostic and procedure codes (2007 to 2017), audiometry, self-reported outcomes up to 6 months postfitting, and HA battery orders (to 2017). Data cleaning and preparation was carried out and is discussed with reference to insights that provide potential value to other researchers pursuing similar studies. HA battery order data over time was used to derive a measure of long-term HA use persistence. Descriptive statistics were used to characterize the sample, comparative analyses against other data supported basic validity assessment, and bivariate analyses probed novel associations between patient characteristics and HA use persistence at 2 years postfitting.

RESULTS

Following extensive cleaning and data preparation, the data show plausible characteristics on diverse metrics and exhibit adequate validity based on comparisons with other published data. Further, rates of HA use persistence are favorable when compared against therapy persistence data for other major chronic conditions. The data also show that the presence of certain comorbid conditions (Parkinson's disease, diabetes, arthritis, and visual impairment) are associated with significantly lower HA use persistence, as are prior inpatient admissions (especially among new HA recipients), and that increasing levels of multimorbidity, in general, are associated with decreasing HA use persistence. This is all despite the fact that deriving relevant audiological care-process variables from the available records was not straightforward, especially concerning the definition of the date of HA fitting, and the use of battery ordering data to determine long-term HA use persistence.

CONCLUSIONS

We have shown that utilizing EHRs in audiology has the potential to provide novel insights into clinical practice patterns, audiologic outcomes, and relations between factors pertaining to hearing and to other health conditions in clinical populations, despite the potential pitfalls regarding the lack of control over the variables available and limitations on how the data are entered. We thus conclude that research using EHRs has the potential to be an integral supplement to population-based and epidemiologic research in the field of audiology.

Analyzing Twitter Data to Evaluate People's Attitudes towards Public Health Policies and Events in the Era of COVID-19

Policymakers and relevant public health authorities can analyze people’s attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people’s attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people’s attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people’s attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.

Application of Big data scientific research analysis platform in clinical medical research

With the rapid development of evidence-based medicine, translational medicine, and pharmacoeconomics in China, as well as the country’s strong commitment to clinical research, the demand for physicians’ research continues to increase. In recent years, real-world studies are attracting more and more attention in the field of health care, as a method of post-marketing re-evaluation of drugs, RWS can better reflect the effects of drugs in real clinical settings. In the past, it was difficult to ensure data quality and efficiency of research implementation because of the large sample size required and the large amount of medical data involved. However, due to the large sample size required and the large amount of medical data involved, it is not only time-consuming and labor-intensive, but also prone to human error, making it difficult to ensure data quality and efficiency of research implementation. This paper analyzes and summarizes the existing application systems of big data analytics platforms, and concludes that big data research analytics platforms using natural language processing, machine learning and other artificial intelligence technologies can help RWS to quickly complete the collection, integration, processing, statistics and analysis of large amounts of medical data, and deeply mine the intrinsic value of the data, real-world research in new drug development, drug discovery, drug discovery, drug discovery, and drug discovery. It has a broad application prospect for multi-level and multi-angle needs such as economics, medical insurance cost control, indications/contraindications evaluation, and clinical guidance.

The everyday acoustic environment and its association with human heart rate: evidence from real-world data logging with hearing aids and wearables

We investigate the short-term association between multidimensional acoustic characteristics of everyday ambient sound and continuous mean heart rate. We used in-market data from hearing aid users who logged ambient acoustics via smartphone-connected hearing aids and continuous mean heart rate in 5 min intervals from their own wearables. We find that acoustic characteristics explain approximately 4% of the fluctuation in mean heart rate throughout the day. Specifically, increases in ambient sound pressure intensity are significantly related to increases in mean heart rate, corroborating prior laboratory and short-term real-world data. In addition, increases in ambient sound quality-that is, more favourable signal to noise ratios-are associated with decreases in mean heart rate. Our findings document a previously unrecognized mixed influence of everyday sounds on cardiovascular stress, and that the relationship is more complex than is seen from an examination of sound intensity alone. Thus, our findings highlight the relevance of ambient environmental sound in models of human ecophysiology.

Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework

Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.

Insights Into Conducting Audiological Research With Clinical Databases

Purpose The clinical data stored in electronic health records (EHRs) provide unique opportunities for audiological clinical research. In this article, we share insights from our experience of working with a large clinical database of over 730,000 cases. Method Under a framework outlining the process from patient care to researcher data use, we describe issues that can arise in each step of this process and how we overcame specific issues in our data set. Results Correct interpretation of findings depends on an understanding of the data source and structure, and efforts to establish confidence in the data through the processes are discussed under the framework. Conclusion We conclude that EHRs have considerable utility in audiological research, though researchers must exhibit caution and consideration when working with EHRs.

Hearing Care Providers' Perspectives on the Utility of Datalogging Information

Purpose The aim of the study was to learn (a) how datalogging information is being used in clinical practice by hearing care providers (HCPs) in the United States and (b) HCPs' opinions about how information collected through the hearing aids could be broadened in clinical application. Method A mixed-method approach was undertaken consisting of an online quantitative survey and qualitative structured telephone interviews. Survey data were analyzed using descriptives and chi-square analyses. The interview data were transcribed and analyzed using inductive content analysis. Results In total, 154 HCPs completed the survey, of whom 10 also completed an interview. Survey data showed that most HCPs use datalogging for conventional applications, such as counseling and fine-tuning during a hearing aid trial. Interview data highlighted four additional desirable datalogging features: (a) data about the sound environment, (b) details about operational aspects of hearing aid use, (c) data about use and nonuse, and (d) automated diagnosis of a hearing aid malfunction. HCPs also envisaged using datalogging in novel ways, such as for demonstrating hearing aid value and supporting decision making. Conclusions Today, datalogging is primarily used as a tool for counseling clients about hours and patterns of hearing aid use and for troubleshooting and fine-tuning. However, HCPs suggested novel and more ambitious uses of datalogging such as for sending alerts about nonuse, for automated diagnosis of a hearing aid malfunction, and for helping the client in their decision making. It remains to be seen whether in the future these will be implemented into clinical practice.