The North America Patient Generated Health Data market report offers a thorough competitive analysis, mapping key players’ strategies, market share, and business models. It provides insights into competitor dynamics, helping companies align their strategies with the current market landscape and future trends.

This statistic displays the result of survey in which physicians in England were asked how they expected digitally generated patient data to likely affect patient care as of 2018. According to the results, 81 percent of physicians expected that patient generated digital data(eg from mobile phone apps and sensor devices) would allow them to provide more personalized care plans for patients.

Organizations can license synthetic, structured data generated by Syntegra from electronic health record systems of community hospitals across the United States, reaching beyond just claims and Rx data.

The synthetic data provides a detailed picture of the patient's journey throughout their hospital stay, including patient demographic information and payer type, as well as rich data not found in any other sources. Examples of this data include: drugs given (timing and dosing), patient location (e.g., ICU, floor, ER), lab results (timing by day and hour), physician roles (e.g., surgeon, attending), medications given, and vital signs. The participating community hospitals with bed sizes ranging from 25 to 532 provide unique visibility and assessment of variation in care outside of large academic medical centers and healthcare networks.

Our synthetic data engine is trained on a broadly representative dataset made up of deep clinical information of approximately 6 million unique patient records and 18 million encounters over 5 years of history. Notably, synthetic data generation allows for the creation of any number of records needed to power your project.

EHR data is available in the following formats: — Cleaned, analytics-ready (a layer of clean and normalized concepts in Tuva Health’s standard relational data model format — FHIR USCDI (labs, medications, vitals, encounters, patients, etc.)

The synthetic data maintains full statistical accuracy, yet does not contain any actual patients, thus removing any patient privacy liability risk. Privacy is preserved in a way that goes beyond HIPAA or GDPR compliance. Our industry-leading metrics prove that both privacy and fidelity are fully maintained.

— Generate the data needed for product development, testing, demo, or other needs — Access data at a scalable price point — Build your desired population, both in size and demographics — Scale up and down to fit specific needs, increasing efficiency and affordability

Syntegra's synthetic data engine also has the ability to augment the original data: — Expand population sizes, rare cohorts, or outcomes of interest — Address algorithmic fairness by correcting bias or introducing intentional bias — Conditionally generate data to inform scenario planning — Impute missing value to minimize gaps in the data

ABSTRACT A lot amounts of data i.e information that related to make wonders with work is called as 'BIG DATA' Last two decades big data treated as a special interest and had a lot potentiality because of hidden features in it. To generate, store, and analyze big data with an aim to improve the services they provide in multiple no of small & large scale industries. As we are considering the health care industry for this big data is providing multiple opportunities like records of patients, inflow & outflow of the hospitals. It also generates a significant portion of big data relevant to public healthcare in biomedical research. In order to derive meaningful information analysis & proper management of data is required. In the haystack seeking solution in big data will be quickly analyzable just like finding a needle. in big data analysis various challenges associated with each step of handling big data surpassed by using high-end computing solutions. for improving public health healthcare providers provide relevant solutions & to systematically generate and analyze big data requirements to be fully loaded with efficient infrastructure. in big data can change the game by opening new avenues for modern healthcare with an efficient management, analysis, and interpretation. vigorous instructions are given by the various industries like public sectors followed by healthcare for the betterment of services and as well as financial upgrades. by taking the revolution in healthcare industry we can accommodate personnel medicine included by therapies in strong integration manner. Keywords: Healthcare, Biomedical Research, Big Data Analytics, Internet of Things, Personalized Medicine, Quantum Computing Cite this Article: Krishnachaitanya.Katkam and Harsh Lohiya, Patient Centric Management Analysis and Future Prospects in Big Data Healthcare, International Journal of Computer Engineering and Technology (IJCET), 13(3), 2022, pp. 76-86.

The global Patient Record Quality Control market is experiencing robust growth, driven by increasing healthcare data volumes, stringent regulatory compliance mandates (like HIPAA and GDPR), and the rising adoption of electronic health records (EHRs). The market's complexity necessitates sophisticated quality control measures to ensure data accuracy, completeness, and consistency for effective patient care and research. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors, including the increasing prevalence of chronic diseases necessitating detailed and accurate medical records, the growing focus on improving healthcare operational efficiency, and the expanding use of data analytics in healthcare for predictive modeling and improved patient outcomes. The inpatient medical record quality control segment currently holds a significant market share, owing to the higher volume of data generated in inpatient settings. However, the outpatient segment is projected to witness faster growth due to the increasing adoption of telehealth and remote patient monitoring, resulting in a substantial increase in electronically generated outpatient records. Hospitals currently dominate the application segment, but clinics are witnessing rapid adoption of advanced quality control solutions. Leading companies like Huimei, BaseBit, Lantone, and Goodwill are actively investing in research and development to enhance their offerings and cater to the growing demand for advanced data quality control features, such as automated error detection, intelligent data validation, and real-time data monitoring. Geographic expansion, particularly in emerging markets of Asia-Pacific and Latin America, presents significant growth opportunities for market players. Despite the positive outlook, challenges like high initial investment costs associated with implementing advanced quality control systems and the need for skilled personnel to manage these systems pose potential restraints to market growth. Future advancements in artificial intelligence (AI) and machine learning (ML) are expected to further automate quality control processes, streamlining workflows and reducing errors, thereby further boosting market expansion.

Introduction: Current health care delivery relies on complex, computer-generated risk models constructed from insurance claims and medical record data. However, these models produce inaccurate predictions of risk levels for individual patients, do not explicitly guide care, and undermine health management investments in many patients at lesser risk. Therefore, this study prospectively validates a concise patient-reported risk assessment that addresses these inadequacies of computer-generated risk models. Methods: Five measures with well-documented impacts on the use of health services are summed to create a "What Matters Index." These measures are: 1) insufficient confidence to self-manage health problems, 2) pain, 3) bothersome emotions, 4) polypharmacy, and 5) adverse medication effects. We compare the sensitivity and predictive values of this index with two representative risk models in a population of 8619 Medicaid recipients. Results: The patient-reported "What Matters Index" and the conventional risk models are found to exhibit similar sensitivities and predictive values for subsequent hospital or emergency room use. Furthermore, the "What Matters Index" is also reliable: akin to its performance during development, for patients with index scores of 1, 2, and ≥3, the odds ratios (with 95% confidence intervals) for subsequent hospitalization within 1 year, relative to patients with a score of 0, are 1.3 (1.1-1.6), 2.0 (1.6-2.4), and 3.4 (2.9-4.0), respectively; for emergency room use, the corresponding odds ratios are 1.3 (1.1-1.4), 1.9 (1.6-2.1), and 2.9 (2.6-3.3). Similar findings were replicated among smaller populations of 1061 mostly older patients from nine private practices and 4428 Medicaid patients without chronic conditions. Summary: In contrast to complex computer-generated risk models, the brief patient-reported "What Matters Index" immediately and unambiguously identifies fundamental, remediable needs for each patient and more sensibly directs the delivery of services to patient categories based on their risk for subsequent costly care.

Objectives: Activity trackers hold the promise to support people in managing their health through quantified measurements about their daily physical activities. Monitoring personal health with quantified activity tracker-generated data provides patients with an opportunity to self-manage their health. Many activity tracker user studies have been conducted within short time frames, however, which makes it difficult to discover the impact of the activity tracker’s novelty effect or the reasons for the device’s long-term use. This study explores the impact of novelty effect on activity tracker adoption and the motivation for sustained use beyond the novelty period.

Materials and Methods: This study uses a mixed-methods approach that combines both quantitative activity tracker log analysis and qualitative one-on-one interviews to develop a deeper behavioral understanding of 23 Fitbit device users who have used their trackers for at least two months (range of use = 69 - 1073 days).

Res...

The dataset has 2 populations of Synthea synthetic patients generated by Synthea tool. Each population has 15K patients with original medical records in CSV files. Because the total file size is >3GB in each population, the files are compressed in zip file. Synthea records are in domains similar to those in real EMR, including patients, encounters, conditions (diagnosis), observations, medications, and procedures. The data was first used in building ML models for lung cancer risk prediction. For more information, see the published paper in Nature Scientific Reports (https://www.nature.com/articles/s41598-022-23011-4)

Increasingly, patient-generated safety insights are shared online, via general social media platforms or dedicated healthcare fora which give patients the opportunity to discuss their disease and treatment options. We evaluated three areas of potential interest for the use of social media in pharmacovigilance. To evaluate how social media may complement existing safety signal detection capabilities, we identified two use cases (drug/adverse event [AE] pairs) and then evaluated the frequency of AE discussions across a range of social media channels. Changes in frequency over time were noted in social media, then compared to frequency changes in Food and Drug Administration Adverse Event Reporting System (FAERS) data over the same time period using a traditional disproportionality method. Although both data sources showed increasing frequencies of AE discussions over time, the increase in frequency was greater in the FAERS data as compared to social media. To demonstrate the robustness of medical/AE insights of linked posts we manually reviewed 2,817 threads containing 21,313 individual posts from 3,601 unique authors. Posts from the same authors were linked together. We used a quality scoring algorithm to determine the groups of linked posts with the highest quality and manually evaluated the top 16 groups of posts. Most linked posts (12/16; 75%) contained all seven relevant medical insights assessed compared to only one (of 1,672) individual post. To test the capability of actively engage patients via social media to obtain follow-up AE information we identified and sent consents for follow-up to 39 individuals (through a third party). We sent target follow-up questions (identified by pharmacovigilance experts as critical for causality assessment) to those who consented. The number of people consenting to follow-up was low (20%), but receipt of follow-up was high (75%). We observed completeness of responses (37 out of 37 questions answered) and short average time required to receive the follow-up (1.8 days). Our findings indicate a limited use of social media data for safety signal detection. However, our research highlights two areas of potential value to pharmacovigilance: obtaining more complete medical/AE insights via longitudinal post linking and actively obtaining rapid follow-up information on AEs.

The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ).

The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses.

All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures.

The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.

The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ).

The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses.

All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures.

The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

35694 rows

154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study

Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities

Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month

Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.

A comparison of some state-of-the-art Digital Twin research papers in healthcare.

The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

This dataset contains 10,000 synthetic patient records representing a scaled-down US Medicare population. The records were generated by Synthea ( https://github.com/synthetichealth/synthea ) and are completely synthetic and contain no real patient data. This data is presented free of cost and free of restrictions. Each record is stored as one file in HL7 FHIR R4 ( https://www.hl7.org/fhir/ ) containing one Bundle, in JSON. For more information on how this specific population was created, or to generate your own at any scale, see: https://github.com/synthetichealth/populations/tree/master/medicare

The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the OMOP Common Data Model format. SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and government (although a citation would be appreciated). You can read our first academic paper here: https://doi.org/10.1093/jamia/ocx079

Click on a hospital ID or zoom in to see information about a hospital and bed occupancy. Click on a hospital to see data about patients. This dashboard is an example of bringing together different datasets and separate tables by tying them together through Hospital ID. Patient records have no spatial information related to them, but the ID allows us to map and visualise the data as well as keeping track of what patient needs are. When someone is ready to be discharged, their care can be handed over to the most suitable organisation or division of the NHS in confidence, knowing exactly where a patient would need to go and being able to supply them with that information. This will help reduce the risk of readmission, as patients feel a continuous support and care throughout their recovery journey.Dashboard contains Living Atlas data as well as openly sourced OS and NHS data. Bed occupancy and availability, as well as patient information are all generated randomly.

Healthcare utilization summary.

We developed an Australianised version of Synthea. Synthea is a synthetic data generation software that uses publicly available population aggregate statistics such as demographics, disease prevalence and incidence rates, and health reports. Synthea generates data based on manually curated models of clinical workflows and disease progression that cover a patient’s entire life and does not use real patient data; guaranteeing a completely synthetic dataset. We generated 117,258 synthetic patients from Queensland.

The size of the Global Patient Portal Market was valued at USD 3.4 billion in 2023 and is projected to reach USD 11.79 billion by 2032, with an expected CAGR of 19.44 % during the forecast period. With technological advancements in healthcare, a growing demand for patient engagement, and the need for effective healthcare management, the market for global patient portals is expected to grow exponentially. Patient portals are a secure online route by which a patient can view his or her health records, schedule appointments, communicate with their providers, order prescriptions, or track their medical history. These patient portals have proven to be catalysts for more proactive engagement from patients in self-care, thereby generally leading to healthier outcomes and greater satisfaction levels among patients. The market is primarily led by the adoption of EHR systems, which are coupled with patient portals to organize their workflow and improve access to their information. HealthCare providers are increasingly implementing patient portals as one means of enhancing communication, administrative costs, and compliance with the Health Information Technology for Economic and Clinical Health (HITECH) Act. Furthermore, the shift toward value-based care and patient-centered models of care makes healthcare organizations pay more heed to digital tools as the only way to improve the engagement rate of patients. The market in North America is expected to generate the highest share of patients, primarily due to higher healthcare spending, sound technological infrastructure, and support by regulation.