Genomic Results Discrete Data Integration Market Outlook

As per our latest research, the global Genomic Results Discrete Data Integration market size reached USD 1.45 billion in 2024, demonstrating robust momentum driven by the increasing adoption of precision medicine and advanced data analytics in genomics. The market is projected to expand at a CAGR of 13.2% during the forecast period, reaching an estimated USD 4.14 billion by 2033. This impressive growth trajectory is fueled by the convergence of high-throughput sequencing technologies, the rising demand for integrated healthcare data, and the need for actionable insights from complex genomic datasets.

A primary growth factor in the Genomic Results Discrete Data Integration market is the exponential rise in genomic data generation, propelled by advancements in next-generation sequencing (NGS) and other high-throughput technologies. As the cost of sequencing continues to decline, the volume of raw genomic data produced by research laboratories, clinical settings, and biopharmaceutical companies has surged. However, the true value of this data is only realized when disparate datasets—spanning genomics, transcriptomics, proteomics, and metabolomics—are seamlessly integrated and analyzed. The integration of discrete genomic results enables researchers and clinicians to uncover complex biological relationships, identify novel biomarkers, and support the development of targeted therapies, thus driving widespread adoption of data integration platforms and solutions.

Another significant driver is the increasing focus on personalized medicine, which relies heavily on the integration of multi-omics data to tailor medical treatments to individual patients. Healthcare providers and pharmaceutical companies are leveraging integrated genomic data to stratify patient populations, predict disease susceptibility, and optimize therapeutic interventions. This shift toward data-driven healthcare is further supported by regulatory agencies encouraging the use of real-world evidence and integrated datasets for drug approval and post-market surveillance. Consequently, the demand for robust, scalable, and interoperable data integration solutions is surging, as stakeholders seek to harness the full potential of genomic and related datasets for clinical and research applications.

Furthermore, the Genomic Results Discrete Data Integration market benefits from technological innovations in artificial intelligence (AI), machine learning (ML), and cloud computing. These technologies facilitate the efficient aggregation, harmonization, and analysis of massive and heterogeneous datasets, overcoming traditional barriers to data integration such as data silos, format inconsistencies, and security concerns. The adoption of AI-driven analytics and cloud-based integration platforms is accelerating, enabling real-time data sharing, collaborative research, and scalable storage solutions. These advancements are not only enhancing the accuracy and speed of data interpretation but also democratizing access to integrated genomic insights across diverse healthcare and research environments.

From a regional perspective, North America continues to dominate the Genomic Results Discrete Data Integration market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to its advanced healthcare infrastructure, significant investments in genomics research, and the presence of leading biopharmaceutical and technology companies. Meanwhile, Asia Pacific is emerging as the fastest-growing region, propelled by expanding genomic research initiatives, increasing healthcare expenditure, and government support for precision medicine. Europe also demonstrates steady growth, driven by collaborative research projects and strong regulatory frameworks supporting data integration. Latin America and Middle East & Africa represent nascent but promising markets, with growing awareness and gradual adoption of integrated genomic solutions.

Component Analysis

The Com

Modelling and simulation studies have been used to inform the choices and development of quality improvement (QI) initiatives in health care, for example, by helping refine the intervention to be implemented or support decisions around the management of demand and capacity. We do not know whether a modelling study can itself be informed by a QI project and what are the associated benefits and challenges. In this research, we sought to investigate the opportunities and challenges associated with an ongoing health service-led QI project in informing the development of a stochastic simulation-based decision support tool to inform decisions around the commissioning of anticoagulation services for patients with atrial fibrillation. We found that the positive synergies offered by the QI project included good access to stakeholders and envisaged end users, co-producing relevant and impactful scenarios for experimentation, as well as access to good quality individual patient level data. On the other hand, substantial effort was required to populate input parameters with values that pertain to the natural history of the disease and the effectiveness of the different treatments. Our findings indicate that, if stakeholders require modelling results to inform aspects of a QI project, upfront investment is needed to ensure timely interaction between the two studies.

TPM 2.0 Discrete Module Market Outlook

According to our latest research, the TPM 2.0 Discrete Module market size reached USD 1.47 billion in 2024, with a robust CAGR of 10.2% anticipated through the forecast period. By 2033, the market is expected to attain a value of USD 3.58 billion, driven by escalating cybersecurity concerns, regulatory mandates, and the proliferation of connected devices. The market’s impressive expansion is underpinned by the increasing adoption of hardware-based security solutions across diverse sectors, emphasizing the critical role of TPM 2.0 modules in safeguarding sensitive data and ensuring platform integrity.

One of the primary growth drivers for the TPM 2.0 Discrete Module market is the surge in cybersecurity threats and the corresponding demand for robust hardware-based security mechanisms. Enterprises and governments globally are prioritizing secure boot processes, encrypted storage, and trusted platform authentication to counteract sophisticated cyberattacks. TPM 2.0 modules, with their advanced cryptographic capabilities, have become essential in protecting sensitive information, particularly as digital transformation accelerates across industries. The evolution of regulatory frameworks, such as GDPR in Europe and CCPA in California, further compels organizations to adopt compliant security solutions, thereby fueling the adoption of TPM 2.0 discrete modules.

Another significant factor driving market growth is the proliferation of connected devices and the expansion of the Internet of Things (IoT) ecosystem. As billions of devices become interconnected, the attack surface for cybercriminals widens, necessitating robust security at the hardware level. TPM 2.0 discrete modules are increasingly being integrated into consumer electronics, automotive systems, and industrial equipment to ensure secure device authentication, firmware integrity, and encrypted communications. The automotive sector, in particular, is witnessing rapid adoption due to the need for secure vehicle-to-everything (V2X) communications and compliance with emerging cybersecurity standards for connected vehicles.

The ongoing digital transformation across banking, healthcare, and government sectors is also propelling the TPM 2.0 Discrete Module market. Financial institutions are leveraging TPM 2.0 modules to secure online transactions, digital identities, and sensitive customer data. In healthcare, the modules are integral to protecting patient records, medical devices, and infrastructure from cyber threats. Government agencies, facing persistent threats from nation-state actors, are increasingly mandating TPM-based security in critical infrastructure and public sector IT systems. These cross-industry trends underscore the indispensable role of TPM 2.0 modules in fortifying trust and resilience in digital ecosystems.

Regionally, Asia Pacific is emerging as the fastest-growing market for TPM 2.0 discrete modules, driven by rapid industrialization, increasing adoption of smart devices, and government-led cybersecurity initiatives. North America remains the largest market, benefiting from early adoption in enterprise IT, automotive, and financial services sectors. Europe follows closely, propelled by stringent data protection regulations and a mature industrial base. While Latin America and the Middle East & Africa are still nascent markets, ongoing digitalization efforts and rising awareness of cybersecurity risks are expected to spur future growth.

Type Analysis

The TPM 2.0 Discrete Module market by type is segmented into Standard TPM 2.0 Modules and Customized TPM 2.0 Modules. Standard modules dominate the market, owing to their widespread adoption across consumer electronics, enterprise IT, and automotive sectors. These modules adhere to industry specifications, ensuring interoperability and simplified integration, which makes them the preferred choice for OEMs and system integrators seeking scalable security solutions. The demand for standard m

NOTES: To accompany deposited data set. Data file includes only the variables reported in: Factors of Prescribing Phage Therapy among UK Healthcare Professionals: Evidence from Conjoint Experiment and Interviews. In the data file the variable SAMPLE designates which platform/sample respondents are from. Prolific Health Professional Sample (HEALTH) and PANELBASE sample of GPs (GP).The data are a wide file format with the results of a discrete choice experiment.

This dataset presents the footprint of the percentage of adults who saw three or more health professionals for the same condition in the preceding 12 months. The data spans the years of 2014-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

Humanitarian crises, particularly in conflict zones, create cascading disruptions that impact every aspect of daily life, including health and disease outcomes. While international humanitarian frameworks categorize these crises into discrete operational clusters, affected populations experience them as interwoven, systemic failures. This study examines how conflict-induced disruptions transform a preventable and typically self-limiting disease—Hepatitis A—into a fatal outcome. Using a systems approach, we seek to characterize the architecture of interconnected disruptions leading to preventable deaths. This study employed the FAIR (Fairness, Agency, Inclusion, and Representation) Framework, a participatory methodology centering community epistemes, to analyze four pediatric cases of Hepatitis A that progressed to fulminant liver failure. Data were obtained through interviews with healthcare providers, caregivers, and community members, supplemented by medical chart reviews. A network-based Architecture of Systems (AoS) map was constructed to visualize interconnections between war-induced systemic disruptions and health outcomes. Network analysis identified key nodes and pathways within the systems map. The findings of this study reveal a complex system of war-driven factors including displacement, destruction of healthcare infrastructure, water scarcity, food deprivation, and fuel blockades that collectively reshaped disease trajectories. Network analysis of the AoS map identified 138 nodes and 231 edges, generating 34,458 pathways linking conflict-related disruptions to health outcomes. Women’s health emerged as a central mediator, with 97% of pathways intersecting with 25 key nodes including women’s roles in caregiving, resource acquisition, and psychological stability. The lack of access to food and clean water, combined with the destruction of healthcare facilities and restrictions on medical evacuation, created conditions where preventable, self-limiting diseases become fatal. This study highlights how conflict restructures health determinants, turning survival strategies into pathways of increasing morbidity and mortality. It also underscores the need for a systems-based humanitarian response that considers the intersecting pathways driving outcomes in crisis settings.

This dataset presents the footprint of the percentage of adults who reported excellent, very good or good health. The data spans the years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

Objective: Information on the preferences of people with asthma for support in managing a flare-up can inform service design which may facilitate appropriate help-seeking. To date, little is known about support preferences for managing a flare-up. The aim of this study was to develop and pilot a discrete choice experiment (DCE) to elicit the preferences of people with asthma with regards to support in managing a flare-up. Methods: Steps in developing the DCE included identification and selection of attributes and levels of the support services, construction of choice tasks, experimental design, construction of DCE instrument, and pretest (n=16) and pilot (n=38) studies of the DCE instrument. A multinomial logit model was used to examine the strength and direction of the six attributes in the pilot study. Results: Our results indicate that from a patient perspective, having a healthcare professional that listens to their concerns was the most valued attribute of support in asthma flare-up management. The other features of support valued by participants were timely access to consultation, a healthcare professional with knowledge of their patient history, a specialist doctor and face-to-face communication. Having a written action plan was the least valued attribute. Conclusions: Our findings suggest patient preference for a model of support in managing their symptoms which includes timely, face-to-face access to a healthcare professional that knows them and listens to their concerns. The findings of the pilot study need to be verified with a larger sample and using models to account for preference heterogeneity.

This dataset presents the footprint of the percentage of adults who felt their GP always or often listened carefully in the preceding 12 months. The data spans the years of 2014-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

This dataset presents the footprint of the percentage of adults who saw a GP more than 12 times in the preceding 12 months. The data spans the years of 2015-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

This dataset presents the footprint of the percentage of adults who did not see or delayed seeing a GP due to cost in the preceding 12 months. The data spans the years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

This dataset presents the footprint of the percentage of adults who felt their GP always or often spent enough time in the preceding 12 months. The data spans the years of 2014-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

This dataset presents the footprint of the percentage of adults who did not see or delayed seeing a dentist, hygienist or dental specialist due to cost in the preceding 12 months. The data spans the years of 2014-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

This dataset presents the footprint of the percentage of adults who saw a GP for urgent medical care in the preceding 12 months. The data spans the years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

BackgroundCOVID-19 has endangered healthcare systems at multiple levels worldwide. Published data suggests that moral dilemmas faced during these unprecedented times have placed physicians at the intersections of ethical and unethical considerations. This phenomenon has questioned the physicians' morality and how that has affected their conduct. The purpose of our review is to tap into the spectrum of the transforming optics of patient care during the pandemic and its impact on psychological wellbeing of physicians.MethodsWe adopted the Arksey and O'Malley's framework, defining research questions, identifying relevant studies, selecting the studies using agreed inclusion and exclusion criteria, charting the data, and summarizing and reporting results. Databases of PubMed/Medline, Web of Science, Scopus, Science Direct, CINAHL, and PsycInfo were searched using a predefined search string. The retrieved titles and abstracts were reviewed. Later, a detailed full-text analysis of the studies which matched our inclusion criteria was performed.ResultsOur first search identified 875 titles and abstracts. After excluding duplicates, irrelevant, and incomplete titles, we selected 28 studies for further analysis. The sample size in 28 studies was 15,509 with an average size of 637 per study. Both quantitative and qualitative approaches were used, with cross-sectional surveys being utilized in all 16 quantitative studies. Using the data from semi-structured interviews, several discrete codes were generated, which led to the identification of five main themes; mental health, individual challenges, decision-making, change in patient care, and support services.ConclusionThis scoping review reports an alarming rise in psychological distress, moral injury, cynicism, uncertainty, burnout, and grief among physicians during the pandemic. Decision-making and patient care were mostly regulated by rationing, triaging, age, gender, and life expectancy. Poor professional controls and institutional services potentially led to physicians' crumbling wellbeing. This research calls for the remediation of the deteriorating mental health and a restoration of medical profession's advocacy and equity.

Demographic characteristics and treatment preferences of unweighted sample (N = 278).

This dataset presents the footprint of the percentage of adults who needed to see a GP but did not in the preceding 12 months. The data spans the years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

This dataset presents the footprint of the percentage of adults who felt they waited longer than acceptable to get an appointment with a GP. The data spans the years of 2013-2014 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.