Preventive healthcare policies in the US: solutions for disease management using Big Data Analytics

Therapeutic interception in individuals at risk of rheumatoid arthritis to prevent clinically impactful disease

Multiple clinical trials for rheumatoid arthritis (RA) prevention have been completed. Here, we set out to report on the lessons learnt from these studies. Researchers who conducted RA prevention trials shared the background, rationale, approach and outcomes and evaluated the lessons learnt to inform the next generation of RA prevention trials. Individuals at risk of RA can be identified through population screening, referrals to musculoskeletal programmes and by recognition of arthralgia suspicious for RA. Clinical trials in individuals at risk for future clinical RA have demonstrated that limited courses of corticosteroids, atorvastatin and hydroxychloroquine do not alter incidence rates of clinical RA; however, rituximab delays clinical RA onset, and methotrexate has transient effects in individuals who are anticitrullinated protein antibody-positive with subclinical joint inflammation identified by imaging. Abatacept delays clinical RA onset but does not fully prevent onset of RA after treatment cessation. Additionally, subclinical joint inflammation and symptoms appear responsive to interventions such as methotrexate and abatacept. To advance prevention, next steps include building networks of individuals at risk for RA, to improve risk stratification for future RA and to understand the biological mechanisms of RA development, including potential endotypes of disease, which can be targeted for prevention, thus adopting a more precision-based approach. Future trials should focus on interceptions aimed at preventing clinical RA onset and which treat existing symptoms and imaging-defined subclinical inflammation. These trials may include advanced designs (eg, adaptive) and should be combined with mechanistic studies to further define pathophysiological drivers of disease development.

Background incidence rates of health outcomes in populations at risk for Lyme disease using US administrative claims data

BACKGROUND

Background incidence rates (IRs) of health outcomes in Lyme disease endemic regions are useful to contextualize events reported during Lyme disease vaccine clinical trials or post-marketing. The objective of this study was to estimate and compare IRs of health outcomes in Lyme disease endemic versus non-endemic regions in the US during pre-COVID and COVID era timeframes.

METHODS

IQVIA PharMetrics® Plus commercial claims database was used to estimate IRs of 64 outcomes relevant to vaccine safety monitoring in the US during January 1, 2017-December 31, 2019 and January 1, 2020-December 31, 2021. Analyses included all individuals aged ≥ 2 years with ≥ 1 year of continuous enrollment. Outcomes were defined by International Classification of Diseases Clinical Modification, 10th Revision (ICD-10-CM) diagnosis codes. IRs and 95 % confidence intervals (CIs) were calculated for each outcome and compared between endemic vs. non-endemic regions, and pre-COVID vs. COVID era using IR ratios (IRR).

RESULTS

The study population included 8.7 million (M) in endemic and 27.8 M in non-endemic regions. Mean age and sex were similar in endemic and non-endemic regions. In both study periods, the IRs were statistically higher in endemic regions for anaphylaxis, meningoencephalitis, myocarditis/pericarditis, and rash (including erythema migrans) as compared with non-endemic regions. Conversely, significantly lower IRs were observed in endemic regions for acute kidney injury, disseminated intravascular coagulation, heart failure, myelitis, myopathies, and systemic lupus erythematosus in both study periods. Most outcomes were statistically less frequent during the COVID-era.

CONCLUSION

This study identified potential differences between Lyme endemic and non-endemic regions of the US in background IRs of health conditions during pre-COVID and COVID era timeframes to inform Lyme disease vaccine safety monitoring. These regional and temporal differences in background IRs should be considered when contextualizing possible safety signals in clinical trials and post-marketing of a vaccine targeted at Lyme disease prevention.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Mobilizing Patient and Public Involvement in the Development of Real-World Digital Technology Solutions: Tutorial

Although the value of patient and public involvement and engagement (PPIE) activities in the development of new interventions and tools is well known, little guidance exists on how to perform these activities in a meaningful way. This is particularly true within large research consortia that target multiple objectives, include multiple patient groups, and work across many countries. Without clear guidance, there is a risk that PPIE may not capture patient opinions and needs correctly, thereby reducing the usefulness and effectiveness of new tools. Mobilise-D is an example of a large research consortium that aims to develop new digital outcome measures for real-world walking in 4 patient cohorts. Mobility is an important indicator of physical health. As such, there is potential clinical value in being able to accurately measure a person's mobility in their daily life environment to help researchers and clinicians better track changes and patterns in a person's daily life and activities. To achieve this, there is a need to create new ways of measuring walking. Recent advancements in digital technology help researchers meet this need. However, before any new measure can be used, researchers, health care professionals, and regulators need to know that the digital method is accurate and both accepted by and produces meaningful outcomes for patients and clinicians. Therefore, this paper outlines how PPIE structures were developed in the Mobilise-D consortium, providing details about the steps taken to implement PPIE, the experiences PPIE contributors had within this process, the lessons learned from the experiences, and recommendations for others who may want to do similar work in the future. The work outlined in this paper provided the Mobilise-D consortium with a foundation from which future PPIE tasks can be created and managed with clearly defined collaboration between researchers and patient representatives across Europe. This paper provides guidance on the work required to set up PPIE structures within a large consortium to promote and support the creation of meaningful and efficient PPIE related to the development of digital mobility outcomes.

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.

The therapeutic concordance approach reduces adverse drug reactions in patients with resistant hypertension

Background

Adverse drug reactions (ADRs) remain among the leading causes of therapy-resistant hypertension (TRH) and uncontrolled blood pressure (BP). We have recently reported beneficial results in BP control in patients with TRH adopting an innovative approach, defined as therapeutic concordance, in which trained physicians and pharmacists reach a concordance with patients to make them more involved in the therapeutic decision-making process.

Methods

The main scope of this study was to investigate whether the therapeutic concordance approach could lead to a reduction in ADR occurrence in TRH patients. The study was performed in a large population of hypertensive subjects of the Campania Salute Network in Italy (ClinicalTrials.gov Identifier: NCT02211365).

Results

We enrolled 4,943 patients who were firstly followed-up for 77.64 ± 34.44 months, allowing us to identify 564 subjects with TRH. Then, 282 of these patients agreed to participate in an investigation to test the impact of the therapeutic concordance approach on ADRs. At the end of this investigation, which had a follow-up of 91.91 ± 54.7 months, 213 patients (75.5%) remained uncontrolled while 69 patients (24.5%, p < 0.0001) reached an optimal BP control. Strikingly, during the first follow-up, patients had complained of a total of 194 ADRs, with an occurrence rate of 68.1% and the therapeutic concordance approach significantly reduced ADRs to 72 (25.5%).

Conclusion

Our findings indicate that the therapeutic concordance approach significantly reduces ADRs in TRH patients.

The Holy grail of healthcare analytics: what it takes to get there?

BACKGROUND

Indian healthcare is rapidly growing and needs efficiency more than ever, which can be achieved by leveraging healthcare analytics. National Digital Health Mission has set the stage for digital health and getting the right direction from the very beginning is important. The current study was, therefore, undertaken to find what it takes for an apex tertiary care teaching hospital to leverage healthcare analytics.

AIM

To study the existing Hospital Information System (HIS) at AIIMS, New Delhi and assess the preparedness to leverage healthcare analytics.

METHODOLOGY

A three-pronged approach was used. First, concurrent review and detailed mapping of all running applications was done based on nine parameters by a multidisciplinary team of experts. Second, capability of the current HIS to measure specific management related KPIs was evaluated. Third, user perspective was obtained from 750 participants from all cadres of healthcare workers, using a validated questionnaire based on Delone and McLean model.

RESULTS

Interoperability issues between applications running within the same institute, impaired informational continuity with limited device interface and automation were found on concurrent review. HIS was capturing data to measure only 9 out of 33 management KPIs. User perspective on information quality was very poor which was found to be due to poor system quality of HIS, though some functions were reportedly well supported by the HIS.

CONCLUSION

It is important for hospitals to first evaluate and strengthen their data generation systems/HIS. The three-pronged approach used in this study provides a template for other hospitals.

Toilet-based continuous health monitoring using urine

Regular health monitoring can result in early detection of disease, accelerate the delivery of medical care and, therefore, considerably improve patient outcomes for countless medical conditions that affect public health. A substantial unmet need remains for technologies that can transform the status quo of reactive health care to preventive, evidence-based, person-centred care. With this goal in mind, platforms that can be easily integrated into people's daily lives and identify a range of biomarkers for health and disease are desirable. However, urine - a biological fluid that is produced in large volumes every day and can be obtained with zero pain, without affecting the daily routine of individuals, and has the most biologically rich content - is discarded into sewers on a regular basis without being processed or monitored. Toilet-based health-monitoring tools in the form of smart toilets could offer preventive home-based continuous health monitoring for early diagnosis of diseases while being connected to data servers (using the Internet of Things) to enable collection of the health status of users. In addition, machine learning methods can assist clinicians to classify, quantify and interpret collected data more rapidly and accurately than they were able to previously. Meanwhile, challenges associated with user acceptance, privacy and test frequency optimization should be considered to facilitate the acceptance of smart toilets in society.

The Linkages Between Reimbursement and Prevention: A Mixed-Methods Approach

Background: The benefits of prevention are widely recognized; ranging from avoiding disease onset to substantially reducing disease burden, which is especially relevant considering the increasing prevalence of chronic diseases. However, its delivery has encountered numerous obstacles in healthcare. While healthcare professionals play an important role in stimulating prevention, their behaviors can be influenced by incentives related to reimbursement schemes.

Purpose: The purpose of this research is to obtain a detailed description and explanation of how reimbursement schemes specifically impact primary, secondary, tertiary, and quaternary prevention.

Methods: Our study takes a mixed-methods approach. Based on a rapid review of the literature, we include and assess 27 studies. Moreover, we conducted semi-structured interviews with eight Dutch healthcare professionals and two representatives of insurance companies, to obtain a deeper understanding of healthcare professionals' behaviors in response to incentives.

Results: Nor fee-for-service (FFS) nor salary can be unambiguously linked to higher or lower provision of preventive services. However, results suggest that FFS's widely reported incentive to increase production might work in favor of preventive services such as immunizations but provide less incentives for chronic disease management. Salary's incentive toward prevention will be (partially) determined by provider-organization's characteristics and reimbursement. Pay-for-performance (P4P) is not always necessarily translated into better health outcomes, effective prevention, or adequate chronic disease management. P4P is considered disruptive by professionals and our results expose how it can lead professionals to resort to (over)medicalization in order to achieve targets. Relatively new forms of reimbursement such as population-based payment may incentivize professionals to adapt the delivery of care to facilitate the delivery of some forms of prevention.

Conclusion: There is not one reimbursement scheme that will stimulate all levels of prevention. Certain types of reimbursement work well for certain types of preventive care services. A volume incentive could be beneficial for prevention activities that are easy to specify. Population-based capitation can help promote preventive activities that require efforts that are not incentivized under other reimbursements, for instance activities that are not easily specified, such as providing education on lifestyle factors related to a patient's (chronic) disease.

Big Data Research in Fighting COVID-19: Contributions and Techniques

The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology.

Measuring outcomes in healthcare economics using Artificial Intelligence: With application to resource management

The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e., Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision-making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial) lead to shifts in planning and budgeting, but most importantly, reduce confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This paper presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.