In 2020, in Europe, it was estimated that a physician's working time is roughly split 50-50 between treating patients and administrative tasks. However, it has been forecast with the implementation of AI technologies in healthcare, physicians would be able to spend ** percent more of their time on patients because the time burden of administrative tasks would be reduced.

This data description contains code (written in the R programming language), as well as processed data and results presented in a research article (see references). No raw data are provided and the data that are made available cannot be linked to study participants. The sample consists of 180 of 308 eligible participants (adult primary care patients in Sweden, living with chronic illness) who responded to a Swedish web-based questionnaire at two time points. Using a confirmatory factor analysis, we calculated latent factor scores for 9 constructs, based on 34 questionnaire items. In this dataset, we share the latent factor scores and the latent profile analysis results. Although raw data are not shared, we provide the questionnaire item, including response scales. The code that was used to produce the latent factor scores and latent profile analysis results is also provided.

The study was performed as part of a research project exploring how the use of eHealth services in chronic care influence interaction and collaboration between patients and healthcare. The purpose of the study was to identify subgroups of primary care patients who are similar with respect to their experiences of co-care, as measured by the DoCCA scale (von Thiele Schwarz, 2021). Baseline data were collected after patients had been introduced to an eHealth service that aimed to support them in their self-care and digital communication with healthcare; follow-up data were collected 7 months later. All patients were treated at the same primary care center, located in the Stockholm Region in Sweden.

Cited reference: von Thiele Schwarz U, Roczniewska M, Pukk Härenstam K, Karlgren K, Hasson H, Menczel S, Wannheden C. The work of having a chronic condition: Development and psychometric evaluation of the Distribution of Co-Care Activities (DoCCA) Scale. BMC Health Services Research (2021) 21:480. doi: 10.1186/s12913-021-06455-8

The DATASET consists of two files: factorscores_docca.csv and latent-profile-analysis-results_docca.csv.

• factorscores_docca.csv: This file contains 18 variables (columns) and 180 cases (rows). The variables represent latent factors (measured at two time points, T1 and T2) and the values are latent factor scores. The questionnaire data that were used to produce the latent factor scores consist of 20 items that measure experiences of collaboration with healthcare, based on the DoCCA scale. These items were included in the latent profile analysis. Additionally, latent factor scores reflecting perceived self-efficacy in self-care (6 items), satisfaction with healthcare (2 items), self-rated health (2 items), and perceived impact of e-health (4 items) were calculated. These items were used to make comparisons between profiles resulting from the latent profile analysis. Variable definitions are provided in a separate file (see below).

• latent-profile-analysis-results_docca.csv: This file contains 14 variables (columns) and 180 cases (rows). The variables represent profile classifications (numbers and labels) and posterior classification probabilities for each of the identified profiles, 4 profiles at T1 and 5 profiles at T2. Transition probabilities (from T1 to T2 profiles) were not calculated due to lacking configural similarity of profiles at T1 and T2; hence no transition probabilities are provided.

The ASSOCIATED DOCUMENTATION consists of one file with variable definitions in English and Swedish, and four script files (written in the R programming language):

• variable-definitions_swe-eng.xlsx: This file consists of four sheets. Sheet 1 (scale-items_original_swedish) specifies the questionnaire items (in Swedish) that were used to calculate the latent factor scores; response scales are included. Sheet 2 (scale-items_translated_english) provides an English translation of the questionnaire items and response scales provided in Sheet 1. Sheet 3 (factorscores_docca) defines the variables in the factorscores_docca.csv dataset. Sheet 4 (latent-profile-analysis-results) defines the variables in the latent-profile-analysis-results_docca.csv dataset.

• R-script_Step-0_Factor-scores.R: R script file with the code that was used to calculate the latent factor scores. This script can only be run with access to the raw data file which is not publicly shared due to ethical constraints. Hence, the purpose of the script file is code transparency. Also, the script shows the model specification that was used in the confirmatory factor analysis (CFA). Missingness in data was accounted for by using Full Information Maximum Likelihood (FIML).

• R-script_Step-1_Latent-profile-analysis.R: R script file with the code that was used to run the latent profile analyses at T1 and T2 and produce profile plots. This code can be run with the provided dataset factorscores_docca.csv. Note that the script generates the results that are provided in the latent-profile-analysis-results_docca.csv dataset.

• R-script_Step-2_Non-parametric-tests.R: R script file with the code that was used to run non-parametric tests for comparing exogenous variables between profiles at T1 and T2. This script uses the following datasets: factorscores_docca.csv and latent-profile-analysis-results_docca.csv.

• R-script_Step-3_Class-transitions.R: R script file with the code that was used to create a sankey diagram for illustrating class transitions. This script uses the following dataset: latent-profile-analysis-results_docca.csv.

Software requirements: To run the code, the R software environment and R packages specified in the script files need to be installed (open source). The scripts were produced in R version 4.2.1.

Ecuador EC: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data was reported at 17.920 % in 2012. Ecuador EC: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data is updated yearly, averaging 17.920 % from Dec 2012 (Median) to 2012, with 1 observations. Ecuador EC: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ecuador – Table EC.World Bank: Health Statistics. The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.; ; National statistical offices or national database and publications compiled by United Nations Statistics Division; ;

Netherlands NL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data was reported at 14.720 % in 2012. This records a decrease from the previous number of 16.940 % for 2006. Netherlands NL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data is updated yearly, averaging 15.070 % from Dec 2001 (Median) to 2012, with 4 observations. The data reached an all-time high of 16.940 % in 2006 and a record low of 14.720 % in 2012. Netherlands NL: Proportion of Time Spent on Unpaid Domestic and Care Work: Female: % of 24 Hour Day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Netherlands – Table NL.World Bank: Health Statistics. The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.; ; National statistical offices or national database and publications compiled by United Nations Statistics Division; ;

Mexico MX: Time Spent Dealing with the Requirements of Government Regulations: % of Senior Management Time data was reported at 13.600 % in 2010. This records a decrease from the previous number of 20.500 % for 2006. Mexico MX: Time Spent Dealing with the Requirements of Government Regulations: % of Senior Management Time data is updated yearly, averaging 17.050 % from Dec 2006 (Median) to 2010, with 2 observations. The data reached an all-time high of 20.500 % in 2006 and a record low of 13.600 % in 2010. Mexico MX: Time Spent Dealing with the Requirements of Government Regulations: % of Senior Management Time data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Company Statistics. Time spent dealing with the requirements of government regulations is the proportion of senior management's time, in a typical week, that is spent dealing with the requirements imposed by government regulations (e.g., taxes, customs, labor regulations, licensing and registration, including dealings with officials, and completing forms).; ; World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/).; Unweighted average;

IntroductionPatient satisfaction with clinical services can have an effect on retention in HIV care and adherence to antiretroviral therapy. This study assessed patient satisfaction and its association with retention and viral suppression in Zambézia Province, Mozambique.MethodsMonthly exit interviews with persons living with HIV were completed from August 2017-January 2019 in 20 health facilities; clinical data were extracted from medical records. Regression analyses assessed the effect of satisfaction scores on retention and viral suppression, adjusting for age, sex, education, civil status, time on treatment, and site. Satisfaction scores were correlated with time spent at health facilities using generalized linear regression models.ResultsData from 4388 patients were analyzed. Overall median satisfaction score was 75% (IQR 53%-84%); median time spent at facilities (from arrival until completion of clinical services) was 2h54min (IQR 1h48min-4h). Overall satisfaction score was not associated with higher odds of retention or viral suppression, but association was seen between satisfaction regarding attention given to patient and respect and higher odds of viral suppression. Patient satisfaction was negatively associated with time spent in facility (Spearman’s correlation -0.63). Increased time spent at facility (from 1 to 3 hours) was not associated with lower retention in care (OR 0.72 [95%CI:0.52–1.01] and 0.83 [95%CI: 0.63–1.09] at 6- and 12-months, respectively), nor with a lower odds of viral suppression (OR 0.96 [95%CI: 0.71–1.32]).ConclusionsStrategies to reduce patient wait times at the health facility warrant continued prioritization. Differentiated models of care have helped considerably, but novel approaches are still needed to further decongest crowded health facilities. In addition, a good client-provider communication and positive attitude can improve patient satisfaction with health services, with an overall improved retention.

Average time spent being physically active, household population by sex and age group.

Hours per week spent providing care to a family member or friend with a long-term illness, disability or aging needs, by sex and main activity of respondent, 2012.

Robotic Process Automation in Healthcare Market Outlook

The Robotic Process Automation (RPA) in Healthcare market is poised for significant growth, projected to increase from a market size of USD 1.3 billion in 2023 to an estimated USD 8.4 billion by 2032, driven by a robust CAGR of 22.4%. This expansion is fueled by the rising need for enhanced operational efficiency and reduced healthcare costs. The healthcare industry is under constant pressure to deliver high-quality care while managing costs. RPA offers a transformative solution by automating repetitive and time-consuming tasks, thereby freeing healthcare professionals to focus on patient care. This shift enhances productivity and reduces the potential for human error, which can lead to significant improvements in patient outcomes and operational efficiency.

A major growth factor driving the RPA in the healthcare market is the increasing demand for automation to handle administrative tasks. Healthcare providers are burdened with extensive documentation and regulatory compliance requirements. RPA can streamline these processes by automating data entry, claims processing, and compliance management, leading to substantial cost savings and improved accuracy. As healthcare providers seek to enhance patient experience and streamline operations, the adoption of RPA solutions is becoming a strategic priority, enabling organizations to redirect resources towards patient care and innovation.

Another significant factor contributing to market growth is the rising need for data integration and management. In an era where data is pivotal to decision-making, healthcare organizations are inundated with vast amounts of data from various sources. RPA helps in seamlessly integrating and managing data from disparate systems, allowing for more informed decision-making and improved patient care. By automating the extraction, processing, and analysis of data, RPA enhances the ability of healthcare providers to deliver personalized and timely services, ultimately leading to better patient outcomes and satisfaction.

Additionally, the technological advancements in AI and machine learning are amplifying the capabilities of RPA in healthcare. These advancements are enabling RPA systems to perform more complex tasks, such as predictive analytics and decision support, which are critical for proactive healthcare management. As AI and machine learning technologies continue to evolve, they will further augment the functionality and efficiency of RPA solutions, making them indispensable tools in the healthcare sector. The integration of these advanced technologies is expected to accelerate the adoption of RPA, driving market growth over the forecast period.

RPA, or Robotic Process Automation, is increasingly being recognized as a game-changer in the healthcare sector. Its ability to automate mundane and repetitive tasks allows healthcare professionals to dedicate more time to patient care, which is the core of healthcare services. By implementing RPA, healthcare facilities can significantly reduce the time spent on administrative tasks, such as patient data management and appointment scheduling. This not only enhances the efficiency of healthcare operations but also improves patient satisfaction by reducing wait times and ensuring timely care delivery. The integration of RPA into healthcare systems is a strategic move that aligns with the industry's goal of providing high-quality, patient-centered care while managing costs effectively.

Regionally, North America is anticipated to hold the largest share of the RPA in healthcare market, driven by the region's advanced healthcare infrastructure and early adoption of innovative technologies. The presence of key market players and supportive government initiatives also contribute to this growth. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate, fueled by increasing healthcare investments, rising patient volumes, and the growing demand for efficient healthcare delivery systems. These regional dynamics are indicative of the global trend towards adopting RPA solutions to improve healthcare delivery and operational efficiency.

Component Analysis

The RPA in healthcare market by component is segmented into software and services, each playing a critical role in the deployment and utilization of RPA solutions. The software segment is a significant contributor to the market, comprising various RPA tools and platforms designed to automate tasks and processes