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

BackgroundA national study, ‘Costing of healthcare services in India’ (CHSI) aimed at generating reliable healthcare cost estimates for health technology assessment and price-setting is being undertaken in India. CHSI sampled 52 public and 40 private hospitals in 13 states and used a mixed micro-costing approach. This paper aims to outline the process, challenges and critical lessons of cost data collection to feed methodological and quality improvement of data collection.MethodsAn exploratory survey with 3 components–an online semi-structured questionnaire, group discussion and review of monitoring data, was conducted amongst CHSI data collection teams. There were qualitative and quantitative components. Difficulty in obtaining individual data was rated on a Likert scale.ResultsMean time taken to complete cost data collection in one department/speciality was 7.86(±0.51) months, majority of which was spent on data entry and data issues resolution. Data collection was most difficult for determination of equipment usage (mean difficulty score 6.59±0.52), consumables prices (6.09±0.58), equipment price(6.05±0.72), and furniture price(5.64±0.68). Human resources, drugs & consumables contributed to 78% of total cost and 31% of data collection time. However, furniture, overheads and equipment consumed 51% of time contributing only 9% of total cost. Seeking multiple permissions, absence of electronic records, multiple sources of data were key challenges causing delays.ConclusionsMicro-costing is time and resource intensive. Addressing key issues prior to data collection would ease the process of data collection, improve quality of estimates and aid priority setting. Electronic health records and availability of national cost data base would facilitate conducting costing studies.

Purpose This study aims to explore the challenges experienced by maternity patients in the selected private and government hospitals in India. Design/methodology/approach The study is cross-sectional and adopts a qualitative research design. The data collection technique uses in-depth interviews of 60 patients to create journey maps for patients visiting a private corporate hospital from Maharashtra and a government medical college in Kerala. Findings Persona 1 (working woman, less than 30 years, first-time delivery) and Persona 2 (housewife, more than 33 years of age and has delivered earlier) were the dominant personas in the private hospital. The pain points experienced included the unavailability of wheelchairs, high waiting time, delay in shifting from the labour room to wards, tasteless food, poor quality of clothing for newborn babies, final bills exceeding the estimated expenditures and limited avenues for registering complaints and grievances. In the government hospital, the dominant personas included Persona 1 (Young, first-time mothers) and Persona 2 (middle aged, with children). The pain points experienced included a lack of awareness about hospital services, dealing with extensive paperwork, the risk of losing medical records, uncomfortable seating arrangements and delays in accessing beds, difficulty in locating the hospital departments and lack of post-pregnancy counselling. Originality/value To the best of the authors’ knowledge, this is one of the first studies comparing the journey of maternity patients in both government and private hospitals in India. The challenges experienced by maternity patients are significantly different in private and public hospitals. In both types of hospitals, first-time mothers face information-related challenges and second-time mothers are more accommodating.

Information about mental health systems is essential for mental health planning to reduce the burden of neuropsychiatric disorders. Unfortunately, many low- and middle-income countries lack systematic information on their mental health systems. The objectives, scope, structure, and contents of mental health assessment and monitoring instruments commonly used in high-income countries may not be appropriate for use in middle- and low-income countries. The World Health Organization (WHO) has recently developed the WHO Assessment Instrument for Mental Health Systems (WHO-AIMS), a comprehensive assessment tool for mental health systems designed for middle- and low-income countries. WHO-AIMS was developed through an iterative process that included input from in-country and international experts on the clarity, content, validity, and feasibility of the instrument, as well as a pilot trial. The resulting instrument, WHO-AIMS 2.2, consists of six domains: policy and legislative framework, mental health services, mental health in primary care, human resources, public information and links with other sectors, and monitoring and research. These domains address the ten recommendations of the World Health Report 2001 through 28 facets and 155 items. All six domains need to be assessed to form a basic, yet broad, picture of a mental health system, with a focus on health sector activities. WHO-AIMS provides essential information for mental health policy and service delivery. Countries will be able to develop information-based mental health policy and plans with clear baseline information and targets. Moreover, they will be able to monitor progress in implementing reform policies, providing community services, and involving consumers, families, and other stakeholders in mental health promotion, prevention, care and rehabilitation. This article provides an overview of the rationale, development process, and potential uses and benefits of WHO-AIMS.

We conducted an electronic, self-administered cross-sectional survey of all ODOs in Canada from November 2021 to January 2022. We targeted key knowledge holders familiar with the data collection processes within each Canadian ODO known to Canadian Blood Services. Categorical item responses are presented as numbers and proportions. We achieved a 100% response rate from 10 Canadian ODOs. Most data were collected by organ donation coordinators. Only 2 of 10 ODOs reported using scripts explaining why sociodemographic data are being collected or incorporated training in cultural sensitivity for any given variable. A lack of cultural sensitivity training was endorsed by 50% of respondents as a barrier to the collection of sociodemographic variables by ODOs, whereas 40% of respondents identified a lack of training in sociodemographic variable collection as a significant barrier. Few programs routinely collect sufficient data to examine health inequities with an intersectional lens. Most data collection occurs midway through the ODO interaction, creating a missed opportunity to better understand differences in social identities of patients who register their intention to donate in advance or who decline the donation. National standardization of equity-relevant data collection definitions and processes of the collection is needed.

Real-time data collection and analysis processes have been designed and an initial capability is demonstrated for the July, 1994 JTMDSN test data. The JTMDSN is a one-year, distributed interactive simulation (DIS) demonstration sponsored by the Defense Modeling and Simulation Office (DMSO). The JTMDSN is built on past TACCSF and RESA DIS accomplishments such as their War Breaker support, to make two more major contributions to the DIS community. First Army, Navy, Air Force, and National Systems interfaced via tactical data links using signal Protocol Data Units (PDUs). Second, the data was collected online from PDUs and reduced to records of key events which could be used to display mission performance and network performance measures in real-time. This paper focuses on this data collection and analysis capability. The JTMDSN DIS Gateway, including the data collection process, was designed and developed by Martin Marietta Corporation. Data analysis processes are being developed by BDM Engineering Services Company. >

Healthcare is becoming the largest growing sector of India because of its huge coverage, providing services and investment by public and private players. In India growth of private hospitals have totally changed the scenario of health care delivery. This study explores the effectiveness of the strategies to provide quality health care and thereby improving the service delivery in Private Hospitals. In total 122 responses were collected after administering the questionnaires. The findings of this study reveals that quality health care strategies has positive impact on service delivery. Quality health care strategies showed a different kind of associations with three measures of quality namely structure, process and outcome measures. The implications from the study provides the need of multifaceted approach for implementing quality improvement strategies and adoption of the model for the same. This study recommends a blend of quality improvement programs with increased ICT (Information and Communication Technology) applications for enhancing the turnaround time. Further study can be conducted on other healthcare quality dimensions and strategic interventions that can enhance the quality of health care and clinical outcomes in Private Hospitals in India.

This study aimed to understand how the Initial Socio-Economic Registration data collection was carried out and to identify the obstacles encountered during its implementation in Karanganyar Regency. Methodology/approach: This study is a survey-based research with a mixed methods approach with a concurrent embedded design. Qualitative and quantitative data were collected in the same time period, but the qualitative approach was predominantly used in this study. The research was conducted at BPS Karanganyar Regency as the main implementing agency of the Initial Socio-Economic Registration Data Collection activities at the kabupaten level. The research data sources used were primary and secondary data. Data collection was conducted through semi-structured interviews with seven key informants (Coordinator, Koseka, PML, and PPL), as well as documentation of regulations, BPS technical guidelines, and regional archives. Data validity was maintained by triangulation between primary and secondary sources. Qualitative data were analyzed using the Miles Huberman interactive model, while quantitative data were analyzed using multiple linear regression. Results/findings: Using George Edward III's implementation theory, the study found that not all implementation variables were met. Key unmet variables included clarity in communication and the adequacy of human resources. Major internal obstacles included late instructions from the central government causing time pressure, insufficient quality of field officers, ineffective training for these officers, and unclear information dissemination to residents. Key external obstacles were public misperception of data collectors and difficulty locating residents. Contribution: This study contributes valuable insights for policymakers and implementing agencies (like BPS at various levels) involved in large-scale socio-economic data collection and social protection programs. It highlights critical practical obstacles in public policy implementation, particularly concerning data collection processes and human resource management in the context of public administration and statistic.

State medical regulatory boards (SMBs) share a common mandate to safeguard patient safety through the effective oversight of licensed medical professionals. Across all U.S. jurisdictions in 2020 SMBs executed more than 7,100 adverse regulatory actions related to 3,342 licensed physicians.1 Yet, to date, SMBs have had no objective methods for determining that the regulatory actions they impose are, in fact, effective at rehabilitating licensees and preventing reoccurrences of similar conduct.This is concerning given the relatively high instance of repeat offenses or “recidivism.” Review of the FSMB’s SMB regulatory action data during the ten-year period from 2011–2020 finds that, on average, 45% of physicians disciplined nationally were first-time recipients of regulatory action.1 More than half of physicians (55%) disciplined during the same 10-year period had previously received at least one public regulatory action. It is unclear how many actions may have arisen from the same instance of misconduct, since the FSMB’s count of actions includes reciprocal actions.In 2019, in what is believed to be the first study of its kind, the North Carolina Medical Board (NCMB) undertook a comprehensive analysis of its disciplinary data. Its goals included gaining insight into the efficacy of its regulatory interventions, with an eye towards reducing recidivism. This work was supported by a generous grant from the FSMB Foundation. NCMB chose to focus its study on cases involving inappropriate opioid prescribing, which is an active enforcement area for North Carolina and most medical regulatory boards.What the project team discovered, ultimately, is that successful data analysis requires a level of forethought, planning and data infrastructure investment that few SMBs — including NCMB — have completed. As a result, the project was unable to complete its objectives. But the process of data collection and analysis during the study period positioned NCMB for more meaningful analysis of enforcement data in the future.We offer a summary of our experience here with the intention of providing lessons learned that might prove valuable for the broader state medical board community. The concept of “data-driven regulation” is an aspiration for many medical regulatory bodies, based on the idea that a better understanding of data can help boards better gauge the efficacy of their interventions, but the implementation of data-driven strategies may be easier said than done.NCMB engaged the expertise of Blaze Advisors, a health care consulting firm specializing in the strategic, tactical and technology services necessary to improve outcomes based in Wilmington, North Carolina, to conduct the data analysis for its study. Blaze looked at 39,286 NCMB licensees (MDs and DOs) with one or more prescribing cases during calendar years 2014–2018, using data exported from NCMB’s database.The firm identified 462 individuals (1.2 %), whose conduct resulted in 538 enforcement cases and 600 total actions. Data analyzed included cases closed with no action, as well as those that resulted in either private or public action. A “case” may be based on a single report of inappropriate prescribing or it may be based on multiple reports from different sources (e.g., patients or family members, pharmacy staff, other prescribers, etc.) that are consolidated into a single enforcement case. Research was initiated in March 2019 and results were reported to NCMB in August 2019.Study objectives included: Create a profile of licensees at risk of prescribing issuesDetermine which regulatory actions are effective in reducing recidivismIdentify gaps in education and trainingEvaluate the disciplinary process to ensure evidence-based regulationThe recidivism project was not able to fulfill these objectives, beyond creating a basic demographic profile of licensees with one or more regulatory actions related to prescribing. Factors that limited analysis included missing data fields, mismatched data, large numbers of free text fields, and duplicated entries in NCMB’s exported data.On further analysis it was determined that NCMB’s method for entering case information during the period studied, 2014–2018, did not allow researchers to easily identify enforcement cases that specifically involved opioids. The reason for this is that most information about each enforcement case is captured in written reports, summaries and notes that cannot be queried or used to generate data reports. These issues limited researchers’ ability to complete fine-detail profiling and meaningful analysis of the efficacy of regulatory actions at addressing deficiencies with opioid prescribing.The larger issue, however, and the ultimate reason the project could not complete desired study objectives, is that NCMB did not build its database with the idea of exporting quality data in mind. Rather, the database was built simply as a means to an end — an administrative tool to allow medical board staff to store and process licensing and enforcement data. It should be noted that the same is likely true for many, if not most, other SMBs, suggesting that other regulatory bodies would face similar challenges if attempting to analyze their data.Fortuitously, NCMB was in the process of planning its transition to a new database vendor when it received the results of its recidivism study from Blaze Advisors. The timing enabled NCMB to incorporate several suggestions to improve data collection into the planning and implementation of its new database system. Principles of sound data collection are explored in the Discussion section of this article.As noted, Blaze Advisors identified 462 licensees (374 men and 88 women) with one or more prescribing cases. Blaze determined that 406 (88%) licensees had a single prescribing case during the studied five-year period. Forty (8.7%) licensees had two prescribing cases between 2014–2018 and 12 (3.6%) of licensees had three cases. Less than 1% of the sample (4 individuals) had four prescribing cases during the period studied.Demographic analysis of the data identified the most common traits of licensees with one or more prescribing cases as a male prescriber over the age of 50. Some 81% of all prescribing cases analyzed involved male physicians (males make up 67% of the total licensed physician population in North Carolina) and just over 65% of all cases involved prescribers aged 50 or older. The highest number of prescribing cases was observed in U.S.-born white male physicians between the ages of 65–79. It must be noted that these data do not necessarily represent an accurate profile of licensees most at risk of engaging in substandard prescribing, but more likely simply reflect the current demographics of the medical profession in North Carolina. NCMB has no plans to use demographic information gathered through this analysis to target specific licensee groups within the state.Our analysis found that prescribing cases were nine times more likely among licensees in urban practice settings than among licensees in rural areas. This was especially striking because the population of the state of North Carolina is distributed at a rate of roughly 2:1 in urban vs. rural areas. The study was not able to establish any direct causal relationship to provide insight as to why prescribing cases were so much more prevalent in urban areas during the studied period.The project had limited ability to assess the impact of regulatory actions, in part because of the relatively small number of actions that occurred during the study period and in part because NCMB’s data did not capture enough information to facilitate study of this topic.It is not enough to simply want to practice data-driven regulation or aspire to evidence-based remediation and discipline. SMBs must also have the data infrastructure to harness the information needed to ask the right questions and devise the right strategies to achieve these aims.To effectively mine enforcement data for insights that can drive improvements to a state medical board regulatory program, it is critical to first assess whether the SMB is set up to capture the right range and depth of information needed to allow meaningful analysis. NCMB’s recidivism project was invaluable because it confirmed that the agency was not yet prepared to engage in the sort of meaningful data-analysis that is necessary to practice evidence-based regulation. NCMB offers this observation to any other SMBs that may be interested in data-driven regulation: In short, it is easier said than done, and substantial planning and investment in a board’s data collection strategy will likely be needed.In September 2021, following its initial study, NCMB engaged Blaze Advisers to collaboratively devise a comprehensive data strategy for the agency. This strategy will be customized to NCMB’s specific needs, goals, and data maturity level.2 The strategy encompasses the following principles:Data is an organizational asset. An agency must understand that its data is not just a byproduct of business activities but an asset with independent value and, potentially, a transformational tool that can support organizational and stakeholder objectives. Embracing this principle is a critical first step for any organization interested in using data to guide and inform regulatory or business activities.Quality data starts at data entry.The process of generating meaningful, usable data starts with ensuring that each raw data point is captured in a standard way. This cannot be emphasized enough. To facilitate data analysis, entered data must be standard, consistent, validated and trusted. Organizations must design software systems and processes to ensure this, beginning with identifying what data points must be captured. Use of free text fields should be kept to the bare minimum and use of pre-populated dropdown menus or checklists should be used wherever possible to ensure accuracy and consistency.These basic foundations are essential and should result in the ability for NCMB or other regulatory organizations to generate meaningful information that can be trusted and used for further inquiry and dimensional analysis. These activities are the basis for data-informed knowledge, not knowledge based on hunch or anecdotal experience, and subsequently leveraged in prescriptive actions to produce or drive toward a desired outcome.Staff training is key.Software and processes are only as good as the people using them. In order to generate quality data, staff who enter raw data must understand the value of accuracy and consistency and should know that their work is helping to create a valuable resource — the organization’s data asset — for their employer. Some may be familiar with the saying, “garbage in, garbage out.” It applies here. Staff who are not properly trained or who are unaware of the value of their work are more likely to make mistakes or introduce inconsistencies to the data.Patience yields results.Designing and implementing a system to collect data and training staff to use and maintain it is just the beginning of becoming a data-driven organization. It takes time to collect enough data to enable the types of analysis that help identify patterns, trends, outliers and other useful information. For example, most new licensee-reported data comes to NCMB through its annual renewal process. Since licensees renew annually during their birth month, it takes a full year to gather updated information from the entire active licensee population. It will take several years to collect enough data to facilitate some types of research.Therefore, embracing data-driven regulation takes time and a commitment to the agency’s unique data governance needs, as well as internal stakeholder involvement.By adopting the principles stated above, NCMB hopes to develop a blueprint that will set the organization up to conduct meaningful analysis of enforcement activity in the future. An additional goal for this initiative is to build a rich and robust culture around reporting and thinking about data collection processes at all levels of the organization.Having gained valuable insights from our 2019 Recidivism Project, NCMB remains optimistic about the potential of data-driven regulation and looks forward to enhancing its ability to harness enforcement data in the service of more effective regulation.

This paper aims to advance the field of data anonymization within the context of Internet of Things (IoT), an environment where data collected may contain sensitive information about users. Specifically, we propose a privacy-preserving data publishing alternative that extends the privacy requirement to the data collection phase. Because our proposal offers privacy-preserving conditions in both the data collecting and publishing, it is suitable for scenarios where a central node collects personal data supplied by a set of devices, typically associated with individuals, without these having to assume trust in the collector. In particular, to limit the risk of individuals' re-identification, the probabilistic k-anonymity property is satisfied during the data collection process and the k-anonymity property is satisfied by the data set derived from the anonymization process. To carry out the anonymous sending of personal data during the collection process, we introduce the delocalized-record chain, a new mechanism of anonymous communication aimed at multi-user environments to collaboratively protect information, which by not requiring third-party intermediaries makes it especially suitable for private IoT networks (besides public IoT networks).

Management of an inventory data within an agency requires an interactive management system to provide ease and informative data presentation. Data processing using an interactive system makes it easy for users to understand and interact with data, especially if the agency concerned is an agency that is in charge of long-term data storage, complex data processing, data collection and data distribution processes, controlling inventory availability, and data collection conducted every day. Data storage and conventional data processing has several weaknesses such as high error rate in data processing, the system is not interactive so that it troubles and impedes the inventory data collection process by users, and can produce data ambiguity that can cause misinformation. By using the inventory control model Just-In-Time (JIT) Inventory method that allows the compilation of data collection and inventory stock requests as needed so that it can easily distribute and minimize wasted stock, the processing, storage, and data collection of a particular data inventory can be controlled well and can provide clearer information. As a result, implementing inventory control methods into a web application can provide web applications that have better functionality in processing, storing, and collecting data.

Aim: Employee training sometimes falls to the bottom of the list of priorities while managing a busy health and social care service due to the abundance of issues to take into account. The greatest human resources may be developed and retained via training, which also offers new skills and information. The entire training practices in private hospitals in India were to be evaluated in this study. The research primarily examines how training affects worker performance, organizational commitment, and the standard of healthcare services.Methodology: A questionnaire was used to gather primary data, and one item was left open-ended to allow for further exploration of the subject.Findings: The study’s results revealed that although employees occasionally receive training, only a small percentage of them think it is of a high enough caliber to have an impact on both their personal life and the company’s. Additionally, they didn’t think that the training had increased their dedication to the organization or changed the way they thought or behaved. They contend that variables other than their connection with their employers have a bigger role in determining their retention rate at a company. Employees acknowledged that certain abilities had improved, which suggests that hospitals utilize a set of broad training techniques that are not altered to meet specific hospital demands. Not every employee said that assessments are conducted on a regular basis. Employees are not involved in the creation of training modules, and they do not believe that training and evaluations have made them stand out for both themselves and their employer. Given that the healthcare industry is always changing, having a professional, educated, and adaptive personnel should always be a top concern. For anybody working in the healthcare industry, training is a must, not just a nice-to-have.Implications/Novel Contribution: The hospitals may utilize these findings to enhance their training initiatives and get the most out of them.

Regional General Hospital, Sigi Regency, has hospital autonomy in an effort to improve service quality. The existence of a supervisory board that independently supervises hospital performance aims to achieve good governance patterns so that health services can be maximally improved. Process. The supervision carried out must be genuinely independent without intervention from other parties to be able to reflect the actual condition of the Tora Belo Regional General Hospital. A phenomenon that often occurs at the Tora Belo Regional General Hospital, Sigi Regency, related to the quality of medical personnel service, shows that there needs to be a more significant gap between the quality of ideal nursing services and actual nurses. This is due to the high demands of patients and the low ability of Human Resources to serve patients. Quality human resource development must pay attention to hospital demand, goals, vision, mission, organizational plans, and HR divisions, as well as the supply of health workers in the community. This research aims to (1). Analyzing the role of the Supervisory Board in providing internal non-technical guidance and supervision of hospitals at the Tora Belo Sigi Regional Hospital, (2) Human Resource Development Strategy in improving the quality of health services at the Tora Belo Regional General Hospital, Sigi Regency, and (3) Analyzing the quality of services in Tora Belo Regional General Hospital, Sigi Regency. The type of research used in this research is descriptive qualitative research, using a purposive sampling method to determine informants. Data analysis takes place simultaneously and is carried out at the same time as the data collection process with a flow of stages: data collection, data reduction, data display, and conclusion or verification (conclusion drawing and verifying). The research results show that: (1) The supervisory board team that was formed and functions as the hospital's governing body in carrying out internal non-technical guidance and supervision of hospitals at Tora Belo Sigi Regional Hospital needs to run optimally according to planned expectations. Some of the problems/obstacles that arise or are faced in forming a board team include creativity barriers, perception barriers, emotional barriers, expression barriers, mental barriers, self-created barriers, conformity and not challenging real things, the habit of judging too quickly, fear of appearing stupid and excessive caution on the part of a person can sometimes unconsciously cause these obstacles to arise which of course will prevent a person from unleashing their creative potential. These obstacles will be unfavorable for the supervisory board team if personnel face these obstacles because the problems faced in audits tend to change and vary. (2) Human resource development at Tora Belo Sigi Regional Hospital has not been carried out in a continuous and planned manner, carefully prepared and based on scientific methods and guided by the skills needed by the hospital currently and in the future, and (3) Optimizing the quality of health services is not only seen through the tangible dimension, empathy, reliability, responsiveness, and assurance, but must also take into account the variable perspectives of consumers, professions, and management

Relevance. The growing integration of Internet of Things (IoT) technologies into all areas of human life – from intelligent households to smart city infrastructure – is accompanied by an exponential increase in the volume of data being collected, transmitted, and processed in real time. When combined with artificial intelligence technologies, this data becomes the foundation for making autonomous decisions, predicting user behavior, and adapting environments to the needs of specific individuals. However, it is precisely in this context that the critically important issue of personal data protection arises. Many IoT devices operate in uncontrolled environments, have limited resources for cryptographic protection, and are vulnerable to cyberattacks and unauthorized data collection. Meanwhile, artificial intelligence algorithms used to analyze this data often exhibit the “black box” problem, where it is impossible to fully explain how and why a particular decision was made based on personalized data. The lack of transparency, combined with broad access to sensitive information, threatens fundamental human rights to privacy. The relevance of this topic is driven by the need to find balanced technical solutions that enable both effective analysis of large-scale data in IoT environments and a high level of data security. In this regard, the study of modern methods for processing, analyzing, and protecting data in IoT systems, adapted to the requirements of ethical artificial intelligence and digital privacy standards, represents one of the key challenges of contemporary digital science. The object of research: the processes of data collection, processing, analysis, and protection in IoT systems, particularly those components related to the use of users' personal information and its processing through artificial intelligence methods. Purpose of the article: research of modern methods for data processing and analysis in IoT systems. The objective of the work is to identify the most effective approaches to secure data handling, characterize existing privacy threats, and assess the potential for integrating protected analytical algorithms that meet both the technical and ethical requirements of the digital environment. Research results. A comprehensive analysis of modern approaches to data collection, processing, analysis, and protection in IoT systems has been conducted, particularly in the context of the growing role of artificial intelligence. The technological foundations of IoT functionality were examined, key architectural components identified, and their role in creating digital ecosystems for monitoring, management, and decision-making across various sectors – from household systems to critical infrastructure – was investigated. Special attention was paid to data preprocessing methods, which help reduce information load, improve the quality of analysis, and adapt data flows to the requirements of intelligent algorithms. It was demonstrated that the use of edge processing and local-level aggregation enhances both system performance and security. The main types of databases for IoT – especially those optimized for time series – were analyzed, along with tools for handling large volumes of data in cloud and hybrid environments. Conclusions. Data collection methods in IoT are multilayered and closely linked to the requirements for energy efficiency, security, latency, and system scalability. The quality and reliability of the collected information form the foundation for subsequent processing, analysis, and decision-making; therefore, the selection of sensors, communication protocols, and architectural models is of strategic importance for any IoT system. Preprocessing and efficient data storage in IoT are critical stages that ensure the quality, security, and usability of information for further analysis. They determine not only the accuracy of analytics but also the stability, scalability, and compliance with regulatory standards. This creates a demand for the development of adaptive, intelligent data processing and storage systems capable of dynamically responding to changes in device operation context and user requirements. The successful implementation of secure IoT solutions requires an integrated approach that combines technical expertise, legal knowledge, and ethical responsibility.

A review of the study processes and protocols afterward by the researchers themselves is scarce. The present study aimed to evaluate the study design and the process of data collection of the Maastricht Intensive Care COVID (MaastrICCht) cohort during the COVID-19 pandemic. This evaluation provides information about the quality of the predefined questions and contributes to transparency in science. Critical appraisal of studies using data from the MaastrICCht cohort. Evaluation of the process of study design and data collection during the COVID-19 pandemic, focusing on the research process and results. From March 2020 to April 2023, all patients diagnosed with COVID-19 admitted to the ICU at Maastricht University Medical Center + (n = 544) were included in the MaastrICCht cohort. In total, 37 studies were carried out until April 2024. Fifteen studies addressed 11 of the 13 predetermined research questions, whereas 22 additional studies were performed based on the initial research questions described in the design. Furthermore, 10 studies were conducted with other researchers in national and international collaboration as a response to new arising questions based on evidence that appeared relevant during the pandemic. Our critical appraisal indicated that using a study protocol enabled many publications and (inter)national collaborations, although formulating pertinent research questions in the context of a novel disease appeared daunting. Despite this, most questions were successfully addressed, whereas few were resolved by other researchers or lost importance due to the expanding body of knowledge.

In India, the private sector diagnoses and treats more patients with TB than the public sector. Gaps in the TB care cascade were observed more among the patients diagnosed in the private sector. The System for TB Elimination in Private Sector (STEPS) model evolved as a solution to address gaps in the quality of care for patients in the private sector by ensuring standards of TB care. STEPS has 3 components: a consortium of private hospitals, a coalition of all professional medical associations, and a STEPS center in each private hospital. STEPS centers act as a single window for notification, linkage for social welfare measures, contact investigation, chemoprophylaxis, direct benefit transfers, and treatment adherence support. STEPS was piloted in 14 districts in the state of Kerala. All 14 districts formed consortiums of private hospital management for policy support and a coalition of professional medical associations for advocacy with doctors. STEPS centers were established in 318 private hospitals. Notification to National TB Elimination Program from the private sector improved by 26% when compared to the previous year. Among the patients notified from the private sector, microbiologically confirmed cases increased by 81%, rifampicin resistance testing at baseline increased by 56%, and the percentage of those informed of their HIV status increased by 95%. The percentage of patients notified from the private sector with their treatment outcome reported improved from 39% (2018) to 99% (2019). The STEPS model demonstrated that a low-cost locally customized private sector engagement model is feasible and is beneficial to society. STEPS could be one of the major solutions for supporting patients reaching the private sector.