After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations. The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities. The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities. For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020. Reported elements include an append of either “_coverage”, “_sum”, or “_avg”. A “_coverage” append denotes how many times the facility reported that element during that collection week. A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week. A “_avg” append is the average of the reports provided for that facility for that element during that collection week. The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”. A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020. Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect. For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied. For recent updates to the dataset, scroll to the bottom of the dataset description. On May 3, 2021, the following fields have been added to this data set. hhs_ids previous_day_admission_adult_covid_confirmed_7_day_coverage previous_day_admission_pediatric_covid_confirmed_7_day_coverage previous_day_admission_adult_covid_suspected_7_day_coverage previous_day_admission_pediatric_covid_suspected_7_day_coverage previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum total_personnel_covid_vaccinated_doses_none_7_day_sum total_personnel_covid_vaccinated_doses_one_7_day_sum total_personnel_covid_vaccinated_doses_all_7_day_sum previous_week_patients_covid_vaccinated_doses_one_7_day_sum previous_week_patients_covid_vaccinated_doses_all_

DSH COVID-19 Patient Testing: Last updated -02/10/2025

DSH COVID-19 Patient Data reports on patient positives and testing counts at the facility level for DSH. The table reports on the following data fields:

• Total patients that tested positive for COVID-19 since 5/16/2020

• Patients newly positive for COVID-19 in the last 14 days

• Patient deaths while patient was positive for COVID-19 since 5/30/2020

• Total number of tests administered since 3/23/2020

Table Notes:

COVID-19 test results for patients include DSH patients who are tested while receiving treatment at an outside medical facility. Data has been de-identified in accordance with CalHHS Data De-identification Guidelines. Counts between 1-10 are masked with "<11". Includes Patients Under Investigation (PUIs) testing and proactive testing of asymptomatic patients for surveillance of geriatric, medically fragile, and skilled nursing facility units and for patients upon admission, re-admission, or discharge. Includes all individuals who were positive for COVID-19 at time of death, regardless of underlying health conditions or whether the cause of death has been confirmed to be COVID-19 related illness. Metro-Norwalk is additional COVID-19 surge space and technically a branch location that is part of DSH Metropolitan Hospital.

The Patient Health Record Analysis market is experiencing robust growth, driven by the increasing volume of healthcare data, the rising adoption of electronic health records (EHRs), and the growing need for improved patient care and operational efficiency. The market's expansion is fueled by advancements in artificial intelligence (AI), machine learning (ML), and big data analytics, which enable more sophisticated analysis of patient data to identify trends, predict risks, and personalize treatment plans. Key players like IBM, Microsoft, and Google are leveraging their technological expertise to develop innovative solutions that support proactive healthcare management, disease prediction, and drug discovery. The market segmentation likely includes solutions based on technology (AI, ML, cloud computing), deployment mode (cloud, on-premise), and end-user (hospitals, clinics, research institutions). Competition is intensifying with numerous companies specializing in specific niches within the market, such as predictive analytics for chronic disease management or image analysis for early disease detection. While the market presents substantial opportunities, challenges remain. Data privacy and security concerns, interoperability issues between different healthcare systems, and the high cost of implementing and maintaining sophisticated analytics solutions are significant hurdles. Furthermore, the need for skilled professionals capable of interpreting complex data analyses and translating findings into actionable insights poses a barrier to wider adoption. Despite these obstacles, the long-term growth outlook remains positive, driven by continued technological advancements, increasing government regulations promoting data-driven healthcare, and the evolving focus on value-based care models. The market is expected to witness consistent expansion over the forecast period, with significant regional variations dependent on technological adoption rates and healthcare infrastructure maturity.

In 2020, ** percent of healthcare providers and ** percent of healthcare payers surveyed in the United States indicated that lack of technical interoperability was the biggest challenge around health data sharing. Among ** percent of providers, noted that timeliness of data that is shared was a challenge, in comparison only ** percent of payers shared the same concern.

The patient health record (PHR) software market is experiencing significant growth, with a CAGR of XX% during the forecast period 2025-2033. The rising emphasis on digitizing healthcare data, increasing government regulations for data privacy and security, and growing demand for personalized medicine are driving this growth. Major trends include the adoption of cloud-based PHR systems, integration with wearable devices and remote patient monitoring platforms, and the use of AI and machine learning for data analysis and personalized care. Market restraints include interoperability challenges, data security concerns, and the cost of implementing and maintaining PHR systems. The PHR software market is segmented by application and type. Major application segments include hospitals, clinics, and home healthcare. Type segments include web-based, mobile-based, and desktop-based PHR systems. Prominent companies operating in the market include Allscripts, Athenahealth, Cerner Corporation, eClinicalWorks, Epic Systems Corporation, GE Healthcare, Greenway Health, Meditech, NextGen Healthcare, CareCloud, CureMD, eMDs, HealthFusion, AdvancedMD, Amazing Charts, Practice Fusion, Kareo, DrChrono, ChARM Health, SimplePractice, WebPT, PrognoCIS, Modernizing Medicine, Practice EHR, and MacPractice. The market is geographically segmented into North America, South America, Europe, Middle East & Africa, and Asia Pacific. North America is the largest market, followed by Europe and Asia Pacific.

Context

Part of Janatahack Hackathon in Analytics Vidhya

Content

The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data, health camps and records, and the treatment of chronic diseases.

MedCamp organizes health camps in several cities with low work life balance. They reach out to working people and ask them to register for these health camps. For those who attend, MedCamp provides them facility to undergo health checks or increase awareness by visiting various stalls (depending on the format of camp).

MedCamp has conducted 65 such events over a period of 4 years and they see a high drop off between “Registration” and number of people taking tests at the Camps. In last 4 years, they have stored data of ~110,000 registrations they have done.

One of the huge costs in arranging these camps is the amount of inventory you need to carry. If you carry more than required inventory, you incur unnecessarily high costs. On the other hand, if you carry less than required inventory for conducting these medical checks, people end up having bad experience.

The Process:

MedCamp employees / volunteers reach out to people and drive registrations.
During the camp, People who “ShowUp” either undergo the medical tests or visit stalls depending on the format of health camp.

Other things to note:

Since this is a completely voluntary activity for the working professionals, MedCamp usually has little profile information about these people.
For a few camps, there was hardware failure, so some information about date and time of registration is lost.
MedCamp runs 3 formats of these camps. The first and second format provides people with an instantaneous health score. The third format provides  
information about several health issues through various awareness stalls.

Favorable outcome:

For the first 2 formats, a favourable outcome is defined as getting a health_score, while in the third format it is defined as visiting at least a stall.
You need to predict the chances (probability) of having a favourable outcome.

Train / Test split:

Camps started on or before 31st March 2006 are considered in Train
Test data is for all camps conducted on or after 1st April 2006.

Acknowledgements

Credits to AV

Inspiration

To share with the data science community to jump start their journey in Healthcare Analytics

Patient Health and Medication Data

Column Descriptions

• Age: The age of the patient in years.
• Medication_Duration: The duration for which the patient has been on the current medication, measured in days.
• Allergy: Indicates the type of allergy the patient has, if any (e.g., Pollen, Peanuts, Shellfish, or None).
• Cholesterol: The patient's cholesterol level, categorized as HIGH or NORMAL.
• Sex: The sex of the patient, categorized as Male (M) or Female (F).
• BP (Blood Pressure): The patient's blood pressure reading, categorized as HIGH, NORMAL, or LOW.
• Na_to_K: The ratio of sodium to potassium in the patient's blood, a numerical value.
• Drug: The specific drug or medication prescribed to the patient, which could be the generic name or brand name of the drug.

Inspired from - https://www.kaggle.com/datasets/prathamtripathi/drug-classification

The "COVID-19 Reported Patient Impact and Hospital Capacity by Facility" dataset from the U.S. Department of Health & Human Services, filtered for Connecticut. View the full dataset and detailed metadata here: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Friday to Thursday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-20 means the average/sum/coverage of the elements captured from that given facility starting and including Friday, November 20, 2020, and ending and including reports for Thursday, November 26, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

A “_coverage” append denotes how many times the facility reported that element during that collection week.

A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.

A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

On May 3, 2021, the following fields have been added to this data set. hhs_ids previous_day_admission_adult_covid_confirmed_7_day_coverage previous_day_admission_pediatric_covid_confirmed_7_day_coverage previous_day_admission_adult_covid_suspected_7_day_coverage previous_day_admission_pediatric_covid_suspected_7_day_coverage previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum total_personnel_covid_vaccinated_doses_none_7_day_sum total_personnel_covid_vaccinated_doses_one_7_day_sum total_personnel_covid_vaccinated_doses_all_7_day_sum previous_week_patients_covid_vaccinated_doses_one_7_day_sum previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added. To see the numbers as reported by the facilities, go to: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday collected fields only. This reflects that these fields are only reported on Wednesdays in a given week.

On 9/20/2021, the following has been updated: The use of analytic dataset as a source.

Electronic Health Records Market Outlook

According to our latest research, the global Electronic Health Records (EHR) market size stood at USD 34.9 billion in 2024, reflecting robust adoption across healthcare systems worldwide. The market is anticipated to progress at a CAGR of 7.3% from 2025 to 2033, reaching an estimated USD 66.1 billion by 2033. This growth is primarily driven by the increasing demand for digital solutions to streamline healthcare delivery, rising government initiatives for health IT infrastructure, and the expanding need for data-driven patient care management.

One of the central growth factors for the Electronic Health Records market is the global push towards digital transformation in healthcare. As healthcare providers strive to improve patient outcomes and operational efficiency, EHR systems have become indispensable for storing, accessing, and analyzing patient data. The integration of advanced technologies such as artificial intelligence, machine learning, and interoperability standards has further accelerated EHR adoption. Governments in developed economies continue to mandate EHR usage, incentivizing providers through funding and regulatory frameworks, which in turn boosts the market’s expansion. Moreover, the COVID-19 pandemic underscored the importance of accessible digital records, further reinforcing the necessity of robust EHR systems.

Another significant driver of the EHR market is the increasing prevalence of chronic diseases and the aging global population. As the number of patients requiring long-term and coordinated care rises, healthcare providers are leveraging EHR solutions to enhance care coordination, reduce medical errors, and ensure continuity of care. The ability to share patient information seamlessly across different care settings is especially vital for managing complex cases. Additionally, the growing focus on value-based care and patient-centric models has led to higher investments in EHR platforms, which facilitate comprehensive data analytics, population health management, and personalized treatment plans.

Furthermore, the rapid proliferation of cloud computing and mobile health technologies is reshaping the Electronic Health Records market. Cloud-based EHR solutions offer scalability, cost-effectiveness, and remote accessibility, making them particularly attractive to small and medium-sized healthcare providers. These solutions enable real-time data sharing, telemedicine integration, and disaster recovery capabilities, all of which are crucial in today’s dynamic healthcare landscape. The shift towards interoperable and user-friendly EHR platforms is also fostering innovation, with vendors introducing customizable solutions tailored to the unique needs of various healthcare settings.

Regionally, North America continues to dominate the Electronic Health Records market, accounting for the largest share in 2024 due to the presence of advanced healthcare infrastructure, favorable government policies, and high EHR adoption rates. However, the Asia Pacific region is poised for the fastest growth, driven by rapid digitalization, increasing healthcare investments, and supportive regulatory initiatives. Europe follows closely, with strong emphasis on data privacy and cross-border health data exchange. Emerging markets in Latin America and the Middle East & Africa are also witnessing increased EHR adoption, albeit at a slower pace due to infrastructural and regulatory challenges.

Product Analysis

The Electronic Health Records market is segmented by product into On-Premise EHR and Cloud-Based EHR, each offering distinct advantages and challenges. On-premise EHR solutions, traditionally favored by large hospitals and healthcare networks, provide organizations with direct control over data security and system customization. These systems are typically installed and maintained within the healthcare provider’s own IT infrastructure, ensuring compliance with stringent regulatory r

Introduction

Electronic Health Records Statistics: In today's fast-paced and data-driven healthcare landscape, Electronic Health Records (EHRs) play a pivotal role in transforming how medical information is stored, accessed, and shared.

EHRs have revolutionized the way healthcare providers deliver patient care by replacing traditional paper-based systems with digital records.

These digital systems enable healthcare professionals to access patient data securely, make informed decisions, and collaborate effectively across the care continuum.

The adoption and utilization of EHR systems have seen significant growth in recent years due to various factors such as government initiatives, advancements in technology, and the increasing need for streamlined healthcare processes.

As EHRs become more prevalent, they offer immense benefits in terms of improved patient outcomes, increased efficiency, and enhanced research opportunities.

The patient health record (PHR) analysis market is projected to experience exponential growth, reaching a value of millions by 2033. This growth is primarily driven by the increasing adoption of electronic health records (EHRs), the growing need for personalized medicine, and the rising prevalence of chronic diseases. Machine learning and natural language processing (NLP) are key technologies driving innovation in PHR analysis, enabling healthcare providers to extract valuable insights from vast amounts of unstructured data. Key market players include Next IT Corporation, iCarbonX, Octopus, Enlitic, Inc., Welltok, IBM Corporation, Microsoft Corporation, General Vision, Inc., Google Inc., Intel Corporation, Nvidia Corporation, Sweetch Health Ltd., Superwise.ai, and others. These companies offer a wide range of solutions, from data aggregation and integration to advanced analytics and reporting. The market is highly competitive, with both established and emerging vendors vying for market share. The adoption of PHR analysis is highest in North America and Europe, followed by Asia-Pacific and the Middle East & Africa. The United States is the largest market for PHR analysis, owing to its well-established healthcare infrastructure and the increasing adoption of EHRs. This comprehensive report provides an in-depth analysis of the patient health record analysis market, covering key industry trends, market size and growth projections, competitive landscape, and emerging technologies.

The Patient Centric Healthcare App Market Report is Segmented by Mode of Operation (Phone-Based, Web-Based, and Hybrid Apps), Application (Wellness Management, and More), End User (Patients Self-Use, and More), Delivery Platform (iOS, Android, Cross-Platform/Progressive Web Apps), and Geography (North America, Europe, Asia-Pacific, Middle East & Africa, South America). The Market Forecasts are Provided in Terms of Value (USD).

IntroductionAccess to health data for patients is hindered by a fragmented healthcare system and the absence of unified, patient-centric solutions. Additionally, there are no mechanics for easy sharing of medical records with healthcare providers, risking incomplete diagnoses. To further intensify the problem, when patients seek care abroad, language barriers may prevent foreign doctors from understanding their health data, further complicating treatment.MethodsOur study presents the development and evaluation of a mobile application designed to enable users to access and share their health records directly from their device, in multiple languages, ensuring ease of use and convenience. The solution utilizes OpenNCP for translating patient summaries into multiple languages and the FHIR Smart Health Links Protocol for secure sharing. We conducted a user acceptance study with 45 participants to evaluate our mobile app's interface and functionality.ResultsThe feedback was positive, highlighting the app's user-friendliness and usefulness. The participants felt it would enhance communication between physicians and patients and the features of sharing and translating are going to give more control of their medical data to the patients.DiscussionBased on the results and participants feedback, our mobile solution significantly enhances healthcare accessibility and efficiency by enabling easy access and sharing of health records in multiple languages, using relevant protocols and standards, reducing medical errors and ensuring personalized care.

Analyze complete patient journeys across both medical and pharmacy claims and accurately track metrics like patient persistence, therapy switches, and concomitant therapies. Medical claims data is sourced from a large health service company with visibility into unblinded provider identities and strong longitudinal integrity allowing for accurate patient journey analytics.

According to a survey conducted in the United States in April 2021, the majority of patients surveyed were very willing to share their health data for various health benefits. The respondents were somewhat/very willing to share their health data with their healthcare provider to receive such things as an early detection of illness and to give a more accurate diagnosis.

As of *************, ** percent of adults surveyed in Canada agreed with the statement that their patient health information (PHI) should be electronically shared among all HCPs involved in their care whenever they need to make decisions about their health. A further ** percent expressed the desire for new and innovative technologies that will allow the HCPs to communicate, access and share their PHI among them.

Demand is emerging for personal health records (PHRs), a patient-centric digital tool for engaging in shared decision-making and healthcare data management. This study uses a RE-AIM framework to explore rural patients and providers’ perceptions prior to and following implementation of a PHR. Health care providers and their patients were recruited from early-adopter patient medical home clinics and a local patient advisory group. Focus groups were used to explore patient and provider pre-implementation perceptions of PHRs and post-implementation provider perspectives. Patients were invited through participating clinics to use the PHR. An implementation process evaluation was conducted. Multiple methods and data sources were used and included pre-/post-intervention patient surveys, provider interviews, and PHR/EHR administrative data. Both patient and provider focus groups described PHRs as providing a comprehensive health story and enhanced communication. Patients prioritized collection of health promotion data while providers endorsed health-related, clinical data. Both groups expressed the need for managing expectations and setting boundaries on PHR use. The evaluation indicated Reach: 16% of targeted patients participated and an additional 127 patients used the PHR as a tool during the COVID-19 pandemic. Effectiveness: Patient satisfaction with use was neutral, with no significant changes to quality of life, self-efficacy, or patients’ activation. Adoption: 44% of eligible clinics participated, primarily those operated publicly versus privately, in smaller communities, and farther from a regional hospital. Implementation: Despite system interoperability expectations, at time of roll out, information exchange standards had not been reached. Additional implementation complications arose from the onset of the pandemic. One clinic on-boarded additional patients resulting in a rapid spike in PHR use. Maintenance: All clinics discontinued PHR within the study period, citing several key barriers to use. RE-AIM offers a valuable process evaluation framework for a comprehensive depiction of impact, and how to drive future success. Interoperability, patient agency and control, and provider training and support are critical obstacles to overcome in PHR implementation.

Global Patient Generated Health Data Market was valued at USD 5.02 Billion in 2023 and is expected to reach USD 7.48 Billion by 2029 with a CAGR of 7.48% during the forecast period.

Introduction

The Personal Health Record (PHR) Software Market is poised for substantial growth, projected to expand from US$ 21.7 billion in 2023 to US$ 9.2 billion by 2033, at a compound annual growth rate of 9%. This sector is undergoing significant transformations, driven by several key factors that enhance its development and user engagement.

One primary growth driver is the increasing emphasis on patient engagement. Modern PHR systems are designed to be user-centric, enhancing communication between patients and providers and enabling patients to manage their own health data effectively. This approach not only aims to improve health outcomes but also seeks to reduce healthcare costs by actively involving patients in their care management.

Further impetus for growth comes from the integration of PHRs with Electronic Health Records (EHRs). This integration allows for seamless management of health information, ensuring continuity of care across various healthcare providers. The increasing sophistication of mobile technology also supports the expansion of PHRs, with mobile apps offering functionalities like appointment scheduling and direct communication with healthcare providers, thus making health data more accessible and usable.

Government initiatives such as the Health Information Technology for Economic and Clinical Health (HITECH) Act have also played a crucial role in promoting PHR adoption by incentivizing healthcare providers to make patient data electronically accessible. This is complemented by advances in data analytics and machine learning, which equip PHRs with tools for disease prediction and health monitoring, enhancing personalized treatment plans and predictive healthcare.

Moreover, the sector is witnessing enhanced measures in privacy and security to protect patient data, complying with federal privacy laws to prevent unauthorized access. Recent industry developments illustrate the sector's evolution: In October 2024, Epic Systems Corporation initiated virtual wards in collaboration with three NHS Foundation Trusts in England, aiming to alleviate hospital capacity issues. This initiative successfully managed acute care for 1,800 patients at home in its first year. In November 2023, Athenahealth and CVS Health launched a joint PHR platform that merges Athena's EHR capabilities with CVS's extensive pharmacy data, providing a holistic view of patient health. Additionally, in June 2022, Oracle Corporation acquired Cerner Corporation for approximately $28.3 billion, a move set to integrate Cerner's health IT solutions into Oracle's cloud infrastructure, promising advancements in healthcare IT and patient care.