Distributed approach to analyze physiological time series signals in medical telemetry

Research in healthcare domain is primarily focused on diseases based on the physiological changes of an individual. Physiological changes are often linked to multiple streams originated from different biological systems of a person. The streams from various biological systems together form attributes for evaluation of symptoms or diseases. The interconnected nature of different biological systems encourages the use of an aggregated approach to understand symptoms and predict diseases. These streams or physiological signals obtained from healthcare systems contribute to a vast amount of vital information in healthcare data. The advent of technologies allows to capture physiological signals over the period, but most of the data acquired from patients are observed momentarily or remains underutilized. The continuous nature of physiological signals demands context aware real-time analysis. The research aspects are addressed in this thesis using large-scale data processing solution. We have developed a general-purpose distributed pipeline for cumulative analysis of physiological signals in medical telemetry. The pipeline is built on the top of a framework which performs computation on a cluster in a distributed environment. The emphasis is given to the creation of a unified pipeline for processing streaming and non-streaming physiological time series signals. The pipeline provides fault-tolerance guarantees for the processing of signals and scalable to multiple cluster nodes. Besides, the pipeline enables indexing of physiological time series signals and provides visualization of real-time and archived time series signals. The pipeline provides interfaces to allow physicians or researchers to use distributed computing for low-latency and high-throughput signals analysis in medical telemetry

Nonfactorizable Contribution to B-Meson Decays to s-Wave Mesons

Two-body weak decays of bottom mesons into two pseudoscalar and pseudoscalar and vector mesons, are examined under isospin analysis to study nonfactorizable contribution

Refining the provider payment system of India’s government-funded health insurance programme: an econometric analysis

Objectives: Reimbursement rates in national health insurance schemes are frequently weighted to account for differences in the costs of service provision. To determine weights for a differential case-based payment system under India’s publicly financed national health insurance scheme, the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PM-JAY), by exploring and quantifying the influence of supply-side factors on the costs of inpatient admissions and surgical procedures. Design: Exploratory analysis using regression-based cost function on data from a multisite health facility costing study—the Cost of Health Services in India (CHSI) Study. Setting: The CHSI Study sample included 11 public sector tertiary care hospitals, 27 public sector district hospitals providing secondary care and 16 private hospitals, from 11 Indian states. Participants: 521 sites from 57 healthcare facilities in 11 states of India. Interventions: Medical and surgical packages of PM-JAY. Primary and secondary outcome measures: The cost per bed-day and cost per surgical procedure were regressed against a range of factors to be considered as weights including hospital location, presence of a teaching function and ownership. In addition, capacity utilisation, number of beds, specialist mix, state gross domestic product, State Health Index ranking and volume of patients across the sample were included as variables in the models. Given the skewed data, cost variables were log-transformed for some models. Results: The estimated mean costs per inpatient bed-day and per procedure were 2307 and 10 686 Indian rupees, respectively. Teaching status, annual hospitalisation, bed size, location of hospital and average length of hospitalisation significantly determine the inpatient bed-day cost, while location of hospital and teaching status determine the procedure costs. Cost per bed-day of teaching hospitals was 38–143.4% higher than in non-teaching hospitals. Similarly, cost per bed-day was 1.3–89.7% higher in tier 1 cities, and 19.5–77.3% higher in tier 2 cities relative to tier 3 cities, respectively. Finally, cost per surgical procedure was higher by 10.6–144.6% in teaching hospitals than non-teaching hospitals; 12.9–171.7% higher in tier 1 cities; and 33.4–140.9% higher in tier 2 cities compared with tier 3 cities, respectively. Conclusion: Our study findings support and validate the recently introduced differential provider payment system under the PM-JAY. While our results are indicative of heterogeneity in hospital costs, other considerations of how these weights will affect coverage, quality, cost containment, as well as create incentives and disincentives for provider and consumer behaviour, and integrate with existing price mark-ups for other factors, should be considered to determine the future revisions in the differential pricing scheme

Cost of hospital services in India: a multi-site study to inform provider payment rates and Health Technology Assessment

The 'Cost of Health Services in India (CHSI)' is the first large scale multi-site facility costing study to incorporate evidence from a national sample of both private and public sectors at different levels of the health system in India. This paper provides an overview of the extent of heterogeneity in costs caused by various supply-side factors. A total of 38 public (11 tertiary care and 27 secondary care) and 16 private hospitals were sampled from 11 states of India. From the sampled facilities, a total of 327 specialties were included, with 48, 79 and 200 specialties covered in tertiary, private and district hospitals respectively. A mixed methodology consisting of both bottom-up and top-down costing was used for data collection. Unit costs per service output were calculated at the cost centre level (outpatient, inpatient, operating theatre, and ICU) and compared across provider type and geographical location. The unadjusted cost per admission was highest for tertiary facilities (₹ 5690, 75 USD) followed by private facilities (₹ 4839, 64 USD) and district hospitals (₹ 3447, 45 USD). Differences in unit costs were found across types of providers, resulting from both variations in capacity utilisation, length of stay and the scale of activity. In addition, significant differences in costs were found associated with geographical location (city classification). The reliance on cost information from single sites or small samples ignores the issue of heterogeneity driven by both demand and supply-side factors. The CHSI cost data set provides a unique insight into cost variability across different types of providers in India. The present analysis shows that both geographical location and the scale of activity are important determinants for deriving the cost of a health service and should be accounted for in healthcare decision making from budgeting to economic evaluation and price-setting

Impact of India's publicly financed health insurance scheme on public sector district hospitals: a health financing perspective

Background: Districts hospitals in India play a pivotal role in delivering health care services in the public sector and are empanelled under India's national health insurance scheme i.e. Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (PMJAY). In this paper, we evaluate the extent to which the PMJAY impacts the district hospitals from a financing perspective. Methods: We used cost data from India's nationally representative costing study—‘Costing of Health Services in India’ (CHSI) to determine the incremental cost of treating PMJAY patients, after adjusting for resources that are paid through supply-side government financing route. Second, we used data on number and claim value paid to public district and sub-district hospitals during 2019, to determine the additional revenue generated through PMJAY. The annual net financial gain per district hospital was estimated as the difference between payments under PMJAY, and the incremental cost of delivering the services. Findings: At current levels of utilisation, the district hospitals in India gain a net annual financial benefit of $26.1 (₹ 1839.3) million, which can potentially increase up to$ 41.8 (₹ 2942.9) million with an increase in the share of patient volume. For an average district hospital, we estimate net annual financial gain of $169,607 (₹ 11.9 million), increasing up to$ 271,372 (₹ 19.1 million) per hospital with increased utilisation. Interpretation: Demand-side financing mechanisms can be used to strengthen the public sector. Increasing utilisation of district hospitals, by either gatekeeping or improving availability of services will enhance financial gains for district hospitals and strengthen public sector. Funding: Department of Health Research, Ministry of Health & Family Welfare, Government of India

Characterizing the Decision Process in Setting Corn and Soybean Seeding Rates

Selecting optimal corn and soybean seeding rates are difficult decisions to make. A survey of Ohio and Michigan farm operators finds that, although generally keen to learn from others, they tend to emphasize their own experience over outside information sources. Soybean growers declare university and extension recommendations as more important than do corn growers. In response to direct queries and in free comments, growers place more emphasis on understanding the agronomic and technological problems at hand than on adjusting to the market environment. Given the decision environment, we argue that these responses are reasonable

CHSI costing study-Challenges and solutions for cost data collection in private hospitals in India

INTRODUCTION: Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals. METHODS: The process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches-an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions. RESULTS: The mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5). DISCUSSION: Cost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection

QUANTIFYING AIR QUALITY, HUMAN HEALTH, AND CLIMATE IMPACTS FROM ENERGY SYSTEMS: ELECTRICITY AND TRANSPORTATION

Thesis (Ph.D.)--University of Washington, 2020Atmospheric emissions from the energy sector contribute to air pollution and climate change. Harmful gases in ambient air degrade air quality; exposure to those gases can lead to health impacts locally and regionally. Greenhouse gases perturb the energy balance of the atmosphere, leading to higher temperatures (global warming) and thus impacting climate at a global scale. Air pollution is linked to exposure disparities among demographic groups (race, income). This dissertation explores air quality, health and climate impacts, and environmental injustice from emissions originating from energy systems. The overarching goals of this research work are to (i) quantify and compare metrics for greenhouse and noxious pollutants to evaluate environmental consequences from interventions, (ii) develop metrics and tools to quantify air quality and human health impacts from point and line sources, (iii) explore distributions of health impacts from air pollution by race, income, and geography, and (iv) demonstrate the use a reduced-complexity air quality model to quantify impacts from multiple energy systems. In this research, I focus on the fine particulate matter (PM2.5) and carbon dioxide (CO2) emissions. PM2.5 is the air pollutant that produces the largest monetized human health impacts in the United States (U.S.) and worldwide. PM2.5 can be directly emitted from combustion or other activities (primary PM2.5) or formed from precursors such as volatile organic compounds (VOCs), sulfur dioxide (SO2), oxides of nitrogen (NOx), and ammonia (NH3) (secondary PM2.5). Concentrations of PM2.5 species in the atmosphere are controlled by emissions, transport, chemistry, and deposition processes. The health impacts are a function of concentrations and the exposed population. Previous research has demonstrated the importance of fine spatial resolution for identifying and quantifying exposure disparities (environmental justice). I used a novel spatial air quality model called “Intervention Model for Air Pollution (InMAP),” combined with epidemiological research concerning air pollution and human health, to estimate health impacts of PM2.5 at a fine resolution. To understand climate impacts, I focus on carbon dioxide (CO2) which is a major greenhouse gas (81% of the total greenhouse gas emissions) emitted from complete combustion of carbon-containing fuels. This dissertation consists of three original studies focused on two energy sectors in the United States (U.S.): electricity generation and freight transportation. The methods employed in this work are based on two approaches: data-driven regression analysis and mechanistic air quality modeling using InMAP. Chapter 2 presents the data-driven empirical approach. Using linear regression between hourly changes in generation and emissions data, I investigate differences between average emission factors (AEFs) and average marginal emission factors (AMEFs) for CO2, SO2, and NOx at different spatial and temporal scales for a Midwest U.S. power market called the Midcontinent Independent System Operator (MISO). AEFs and AMEFs are two commonly used metrics for estimating emission benefits from energy-efficiency strategies. This is the first study that estimates AEFs and AMEFs for a U.S. Regional Transmission Organization (RTO). I find, for example, that marginal emission factors are generally higher during late night and early morning compared to afternoons. In general, AEFs tend to be larger than AMEFs (typical difference: ∼20%), and thus may overestimate emission impacts from interventions in the power sector, relative to using AMEFs. Chapters 3 and 4 present a mechanistic modeling approach for investigating air quality and human health impacts from PM2.5 emissions. Chapter 3 presents a study that estimates exposure to and health impacts of PM2.5 from electricity generation in the U.S., for each of the seven Regional Transmission Organizations (RTOs), for each US state, by income, and by race. This research is the first national-scale investigation of environmental justice aspects of total PM2.5 from electricity generation. I find that average exposures are the highest for blacks, followed by non-Latino whites. Exposures for remaining groups (e.g., Asians, Native Americans, Latinos) are somewhat lower. Levels of disparity differ by state and RTO. Exposures are higher for lower-income than for higher-income, but disparities are larger by race than by income. Geographically, I observe large differences between where electricity is generated and where people experience the resulting PM2.5 health consequences; some states are net exporters of health impacts, other are net importers. Chapter 4 presents a study that investigates environmental health and climate impacts from inter-state road, rail, water, and air freight transportation in the U.S. This is the first detailed study to compare health, environmental justice, and climate impacts of four freight modes, studying each route separately. Average impacts per unit mass shipped are as follows. For all three impacts studied (PM2.5 health effects, racial-ethnic disparities in PM2.5 exposure, CO2 emissions), impacts are greatest for aircraft. Among non-aircraft modes: PM2.5 health effects are largest for rail, intermediate for barge, and lowest for truck; PM2.5 exposure disparities are largest for rail and are lower for truck and barge; climate impacts are largest for truck, intermediate for barge, and lowest for rail. Inter-state freight movement in the U.S. disproportionately impacts white non-Latinos relative to other racial-ethnic groups. This dissertation presents work to investigate air quality, health and climate impacts, and environmental justice-related issues from electricity generation and freight transportation. This work can be extended to other specific sectors of the economy and can be useful to scientists, planners, and policymakers to estimate environmental benefits of energy conservation programs and create policies that address environmental injustice. The metrics developed in this work can be applied by researchers to new electricity and transportation scenarios to understand their impacts and benefits