The average number of hospital beds available per 1,000 people in the United States was forecast to continuously decrease between 2024 and 2029 by in total 0.1 beds (-3.7 percent). After the eighth consecutive decreasing year, the number of available beds per 1,000 people is estimated to reach 2.63 beds and therefore a new minimum in 2029. Depicted is the number of hospital beds per capita in the country or region at hand. As defined by World Bank this includes inpatient beds in general, specialized, public and private hospitals as well as rehabilitation centers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the average number of hospital beds available per 1,000 people in countries like Canada and Mexico.

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Hospital Beds Market Size 2025-2029

The hospital beds market size is forecast to increase by USD 2.69 billion, at a CAGR of 9.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the rise in infectious diseases and the increasing number of medical emergencies. These factors have led to a heightened demand for advanced hospital beds that cater to the specific needs of patients. For instance, bariatric hospital beds are gaining popularity due to the increasing prevalence of obesity and related health issues. Similarly, intensive care unit (ICU) beds are in high demand due to the growing number of critical patients requiring constant monitoring and care. However, the high cost of automated hospital beds poses a significant challenge for market growth. These advanced beds come with advanced features such as adjustable heights, electric mattresses, and integrated technology for patient monitoring.
While these features offer numerous benefits, they also increase the cost of production and, subsequently, the price of the beds. This challenge may limit the adoption of automated hospital beds in some healthcare facilities, particularly in developing countries and low-income regions. Another challenge is the shortage of hospital beds, especially during outbreaks of infectious diseases. For instance, during the COVID-19 pandemic, many hospitals faced a shortage of beds, leading to overcrowding and an increased risk of infection transmission. To address this challenge, some companies have started producing modular and portable hospital beds that can be easily transported and set up in temporary hospitals or quarantine facilities. The demand for home healthcare services is also driving the market, as patients prefer to receive care

What will be the Size of the Hospital Beds Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market encompasses various product offerings, including those with remote bed control, chronic care support, and smart bed technology. Chronic care patients benefit from these advanced beds, which enhance healthcare efficiency and patient comfort. Recyclable materials and corrosion resistance are essential considerations for bed manufacturers, aligning with the industry's sustainability and cost optimization efforts. Data analytics plays a crucial role in hospital bed procurement, enabling supply chain management and clinical outcomes assessment. Lateral rotation and automatic bed turning features cater to acute care and long-term care settings, ensuring patient safety and improving sleep quality. Rental services offer flexibility for healthcare facilities, allowing them to adapt to changing patient needs while minimizing capital expenditures.
Wireless connectivity integration enables patient monitoring and data sharing, enhancing the overall quality of care. Patient safety remains a top priority, with material durability and clinical outcomes being key factors in bed selection. Smart bed technology, including automatic bed turning and home healthcare integration, further improves patient care and satisfaction. In the realm of hospital bed procurement, cost optimization and quality control are essential elements. Lease agreements provide an alternative financing option, enabling healthcare providers to access advanced bed technology while managing budgets effectively. Regardless of the specific market segment, the hospital beds industry continues to evolve, integrating the latest technology and trends to meet the unique needs of healthcare facilities and patients.

How is this Hospital Beds Industry segmented?

The hospital beds industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Manual beds
  Semi-automated beds
  Automated beds


Application

  Intensive care
  Acute care
  Home care


End-user

  Hospitals
  Home healthcare
  Elderly care facilities
  Ambulatory surgical centers


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The manual beds segment is estimated to witness significant growth during the forecast period. The market encompasses various product offerings, including manual and electric beds, bariatric beds, geriatric beds, ICU beds, operating room beds, and recovery room beds. Compliance with regulatory standards is a crucial factor in this market, ensuring easy cleaning, bedside rails, and fall prevention. Manual beds, the largest segment, remain po

Children's Hospitals Pricing Information

Children's Hospitals Pricing Information

By Amber Thomas [source]

About this dataset

This dataset contains machine-readable hospital pricing information for Children's Hospitals and Clinics of Minnesota. It includes three separate files:

1. 2022-top-25-hospital-based-clinics-list.csv: This file provides the top 25 primary care procedure prices, including procedure codes, fees, and insurance coverage details.
2. 2022-standard-list-of-charges-hospital-op.csv: This file includes machine-readable hospital pricing information, including procedure codes, fees, and insurance coverage details.
3. 2022-msdrg.csv: This file also contains machine-readable hospital pricing information, including procedure codes, fees, and insurance coverage details.

The data was collected programmatically using a custom script written in Node.js and Microsoft Playwright. These files were then mirrored on the data.world platform using the Import from URL option.

If you find any errors in the dataset or have any questions or concerns, please leave a note in the Discussion tab of this dataset or contact supportdata.world for assistance

How to use the dataset

• Dataset Overview:

  • The dataset contains three files: a) 2022-top-25-hospital-based-clinics-list.csv: This file includes the top 25 primary care procedure prices for Children's Hospitals and Clinics of Minnesota, including procedure codes, fees, and insurance coverages. b) 2022-standard-list-of-charges-hospital-op.csv: This file includes machine-readable hospital pricing information for Children's Hospitals and Clinics of Minnesota, including procedure codes, fees, and insurance coverages. c) 2022-msdrg.csv: This file includes machine-readable hospital pricing information for Children's Hospitals and Clinics of Minnesota, including MSDRG (Medicare Severity Diagnosis Related Groups) codes, fees, and insurance coverages.

• Data Collection:

  • The data was collected programmatically using a custom script written in Node.js with the assistance of Microsoft Playwright.
  • These datasets were programmatically mirrored on the data.world platform using the Import from URL option.

• Usage Guidelines:

  • Explore Procedure Prices: You can analyze the top 25 primary care procedure prices by referring to the '2022-top-25-hospital-based-clinics-list.csv' file. It provides information on procedure codes (identifiers), associated fees (costs), and insurance coverage details.

  • Analyze Hospital Price Information: The '2022-standard-list-of-charges-hospital-op.csv' contains comprehensive machine-readable hospital pricing information. You can examine various procedures by their respective codes along with associated fees as well as corresponding insurance coverage details.

  • Understand MSDRG Codes & Fees: The '2022-msdrg.csv' file includes machine-readable hospital pricing information based on MSDRG (Medicare Severity Diagnosis Related Groups) codes. You can explore the relationship between diagnosis groups and associated fees, along with insurance coverage details.

• Reporting Errors:

  • If you identify any errors or discrepancies in the dataset, please leave a note in the Discussion tab of this dataset to notify others who may be interested.
  • Alternatively, you can reach out to the data.world team at supportdata.world for further assistance.

Research Ideas

• Comparative Analysis: Researchers and healthcare professionals can use this dataset to compare the pricing of primary care procedures at Children's Hospitals and Clinics of Minnesota with other hospitals. This can help identify any variations or discrepancies in pricing, enabling better cost management and transparency.
• Insurance Coverage Analysis: The insurance coverage information provided in this dataset can be used to analyze which procedures are covered by different insurance providers. This analysis can help patients understand their out-of-pocket expenses for specific procedures and choose the best insurance plan accordingly.
• Cost Estimation: Patients can utilize this dataset to estimate the cost of primary care procedures at Children's Hospitals and Clinics of Minnesota before seeking medical treatment. By comparing procedure fees across different hospitals, patients can make informed decisions about where to receive their healthcare services based on affordability and quality

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**Unknown License - Please chec...

Problem Statement

👉 Download the case studies here

Hospitals and healthcare providers faced challenges in ensuring continuous monitoring of patient vitals, especially for high-risk patients. Traditional monitoring methods often lacked real-time data processing and timely alerts, leading to delayed responses and increased hospital readmissions. The healthcare provider needed a solution to monitor patient health continuously and deliver actionable insights for improved care.

Challenge

Implementing an advanced patient monitoring system involved overcoming several challenges:

Collecting and analyzing real-time data from multiple IoT-enabled medical devices.

Ensuring accurate health insights while minimizing false alarms.

Integrating the system seamlessly with hospital workflows and electronic health records (EHR).

Solution Provided

A comprehensive patient monitoring system was developed using IoT-enabled medical devices and AI-based monitoring systems. The solution was designed to:

Continuously collect patient vital data such as heart rate, blood pressure, oxygen levels, and temperature.

Analyze data in real-time to detect anomalies and provide early warnings for potential health issues.

Send alerts to healthcare professionals and caregivers for timely interventions.

Development Steps

Data Collection

Deployed IoT-enabled devices such as wearable monitors, smart sensors, and bedside equipment to collect patient data continuously.

Preprocessing

Cleaned and standardized data streams to ensure accurate analysis and integration with hospital systems.

AI Model Development

Built machine learning models to analyze vital trends and detect abnormalities in real-time

Validation

Tested the system in controlled environments to ensure accuracy and reliability in detecting health issues.

Deployment

Implemented the solution in hospitals and care facilities, integrating it with EHR systems and alert mechanisms for seamless operation.

Continuous Monitoring & Improvement

Established a feedback loop to refine models and algorithms based on real-world data and healthcare provider feedback.

Results

Enhanced Patient Care

Real-time monitoring and proactive alerts enabled healthcare professionals to provide timely interventions, improving patient outcomes.

Early Detection of Health Issues

The system detected potential health complications early, reducing the severity of conditions and preventing critical events.

Reduced Hospital Readmissions

Continuous monitoring helped manage patient health effectively, leading to a significant decrease in readmission rates.

Improved Operational Efficiency

Automation and real-time insights reduced the burden on healthcare staff, allowing them to focus on critical cases.

Scalable Solution

The system adapted seamlessly to various healthcare settings, including hospitals, clinics, and home care environments.

[215+ Pages Report] The global Private Hospital market was worth USD 820 Billion in 2021 and at a CAGR of 12.5% is expected to reach a value of USD 2000 Billion by 2028.

The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.

In 2023, there were nearly 11 thousand hospitals in Columbia, the highest number among OECD countries, followed by 8,156 hospitals in Japan. If only general hospitals were counted (excluding mental health hospitals and other specialized hospitals), Japan had the most number of general hospitals among OECD countries worldwide. Most countries reported hospitals numbers similar to or lower than the previous year. Meanwhile, Mexico, South Korea and the Netherlands all reported more hospitals than last year.

Global Hospital Electronic Health Records Market size was $20.16 billion in 2024 and is grow to $39.65 billion by 2034, a CAGR of 7.00% by 2025 and 2034.

Internet Hospital Market Outlook

As of 2023, the global market size for internet hospitals is estimated to be worth approximately USD 24 billion, with a projected compound annual growth rate (CAGR) of 21% through 2032, bringing the forecasted market size to around USD 155 billion. Factors such as the increasing adoption of telemedicine, advancements in healthcare technology, and the convenience provided by internet hospitals are major drivers of this growth.

The growth of the internet hospital market is primarily driven by the increasing need for accessible healthcare services. With the global population aging and the prevalence of chronic diseases on the rise, traditional healthcare systems are under immense pressure. Internet hospitals provide a viable solution by offering remote consultations, diagnosis, and treatment options, thus alleviating some of the burdens on conventional healthcare facilities. The convenience and flexibility of accessing healthcare services from the comfort of oneÂ’s home are particularly appealing to patients with mobility issues or those living in remote areas.

Technological advancements also play a crucial role in the expansion of the internet hospital market. The integration of artificial intelligence (AI), machine learning, and big data analytics into healthcare services has significantly improved the accuracy and efficiency of diagnosis and treatment plans. These technologies enable healthcare providers to offer personalized care based on comprehensive data analysis, thereby enhancing patient outcomes. Moreover, the proliferation of smartphones and high-speed internet has made it easier for patients to access these services, further driving market growth.

Another significant factor contributing to the growth of the internet hospital market is the increasing acceptance and adoption of telehealth services by healthcare providers. The COVID-19 pandemic has dramatically accelerated the adoption of telehealth, as it became a necessity to reduce physical contact and curb the spread of the virus. This shift has led to greater familiarity and comfort with telehealth platforms among both healthcare providers and patients, which is expected to continue post-pandemic. Consequently, more healthcare providers are incorporating internet hospital services into their practice, thus expanding the market.

The rise of Online Doctor Medical Service platforms has further propelled the growth of internet hospitals. These services allow patients to consult with healthcare professionals via video calls, chat, or phone, providing a convenient and efficient alternative to traditional in-person visits. Online Doctor Medical Service platforms are particularly beneficial for individuals seeking immediate medical advice or those unable to visit a healthcare facility due to geographical or mobility constraints. The integration of these services into internet hospitals enhances their accessibility and appeal, offering patients a seamless and comprehensive healthcare experience. As more people become comfortable with digital interactions, the demand for Online Doctor Medical Service is expected to increase, contributing significantly to the expansion of the internet hospital market.

Regionally, the Asia Pacific region is expected to witness the highest growth in the internet hospital market, driven by large populations, increasing internet penetration, and supportive government initiatives. Countries like China and India are leading the charge with significant investments in telehealth infrastructure. North America and Europe are also significant markets due to advanced healthcare systems and high levels of digital literacy among the population. Latin America and the Middle East & Africa are gradually adopting internet hospital services, driven by the need to improve healthcare accessibility in underserved regions.

Service Type Analysis

The internet hospital market is segmented by service type into consultation, diagnosis, treatment, monitoring, and others. Each of these service types plays a crucial role in the overall functionality and appeal of internet hospitals. Consultation services are perhaps the most widely recognized and utilized aspect of internet hospitals. These services allow patients to connect with healthcare professionals remotely for advice and preliminary assessments. The convenience of consultation services has led to their widespread adoption, particularly among patients with minor ailments or those seeking s

We are working to develop a comprehensive dataset of surgical tools based on specialities, with a hierarchical structure – speciality, pack, set and tool. We belive that this dataset can be useful for computer vision and deep learning research into surgical tool tracking, management and surgical training and audit. We have therefore created an initial dataset of surgical tool (instrument and implant) images, captured using under different lighting conditions and with different backgrounds. We captured RGB images of surgical tools using a DSLR camera and webcam on site in a major hospital under realistic conditions and with the surgical tools currently in use. Image backgrounds in our initial dataset were essentially flat colours, even though different colour backgrounds were used. As we further developed our dataset, we will try to include much greater occlusions, illumination changes, and the presence of blood, tissue and smoke in the images which would be more reflective of crowded, messy, real-world conditions.

Illumination sources included natural light – direct sunlight and shaded light – LED, halogen and fluorescent lighting, and this accurately reflected the illumination working conditions within the hospital. Distances of the surgical tools to the camera to the object ranged from 60 to 150 cms., and the average class size was 74 images. Images captured included individual object images as well as cluttered, clustered and occluded objects. Our initial focus was on Orthopaedics and General Surgery, two out of the 14 surgical specialities. We selected these specialities since general surgery instruments are the most commonly used tools across all surgeries and provide instrument volume, while orthopaedics provides variety and complexity given the wide range of procedures, instruments and implants used in orthopaedic surgery. We will add other specialities as we develop this dataset, to reflect the complexities inherent in each of the surgical specialities. This dataset was designed to offer a large variety of tools, arranged hierarchically to reflect how surgical tools are organised in real-world conditions.

If you do find our dataset useful, please cite our papers in your work:

Rodrigues, M., Mayo, M, and Patros, P. (2022). OctopusNet: Machine Learning for Intelligent Management of Surgical Tools. Published in “Smart Health”, Volume 23, 2022. https://doi.org/10.1016/j.smhl.2021.100244

Rodrigues, M., Mayo, M, and Patros, P. (2021). Evaluation of Deep Learning Techniques on a Novel Hierarchical Surgical Tool Dataset. Accepted paper at The 2021 Australasian Joint Conference on Artificial Intelligence. 2021. To be Published in Lecture Notes in Computer Science series.

Rodrigues, M., Mayo, M, and Patros, P. (2021). Interpretable deep learning for surgical tool management. In M. Reyes, P. Henriques Abreu, J. Cardoso, M. Hajij, G. Zamzmi, P. Rahul, and L. Thakur (Eds.), Proc 4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2021) LNCS 12929 (pp. 3-12). Cham: Springer.

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Healthcare Information Systems Market Size 2024-2028

The healthcare information systems market size is forecast to increase by USD 126.2 billion at a CAGR of 9.5% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for efficient medical care and disease management. Key features of HIS, such as medical device integration and ease of use, are driving this growth. Remote patient monitoring and disease management are becoming increasingly important, enabling healthcare providers to deliver better patient care and financial savings through improved efficiency. However, technical considerations, including data security and privacy, remain challenges that must be addressed to ensure the successful implementation and adoption of HIS. The market is witnessing a high demand for electronic health record (EHR) solutions and an increasing number of mergers and acquisitions. Despite these opportunities, it is crucial for providers to carefully consider the technical aspects of HIS implementation to ensure seamless integration and optimal performance.

What will be the Size of the Market During the Forecast Period?

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The healthcare industry is undergoing a significant transformation, driven by advancements in technology and the increasing demand for efficient, patient-centric care. The market is witnessing substantial growth as healthcare organizations seek to optimize their operations, improve patient outcomes, and reduce costs. Healthcare data management is a critical component of this transformation. The ability to collect, store, and analyze large volumes of patient data is essential for delivering personalized and precise medical care. Healthcare data analytics is playing an increasingly important role in this regard, enabling healthcare providers to gain valuable insights from patient data and make informed decisions.
In addition, another key trend in the market is healthcare data security. With the increasing digitization of healthcare data, ensuring its security and privacy is a top priority. Healthcare organizations are investing in advanced cybersecurity solutions to protect sensitive patient information from cyber threats. Mobile technology is also transforming the healthcare landscape. Mobile health apps, telehealth platforms, and wearable technology are enabling remote patient monitoring, teleconsultations, and other innovative healthcare services. These technologies are improving patient engagement, enhancing the patient experience, and reducing the need for in-person visits. Cloud-based healthcare systems are another area of growth in the market.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Revenue cycle management
  Hospital information system
  Medical imaging information system
  Pharmacy information systems
  Laboratory information systems


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  Asia

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The revenue cycle management segment is estimated to witness significant growth during the forecast period.

The healthcare industry's shift towards digitalization is driving the adoption of Healthcare Information Systems (HCIS), particularly in patient engagement and managing patient-related data. Chronic diseases, which account for a significant portion of healthcare expenditures, necessitate effective data management and analysis. HCIS product lines, including hardware and healthcare IT solutions, enable healthcare facilities to streamline operations, reduce costs, and enhance patient care. As the US population ages and the prevalence of chronic diseases increases, the need for advanced healthcare data analytics becomes more critical. HCIS solutions help manage complex billing processes, ensuring accuracy and compliance with regulations such as HIPAA and FDCPA.

Get a glance at the market report of share of various segments Request Free Sample

The revenue cycle management segment was valued at USD 81.10 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 47% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

In North America, the market is among the most advanced, driven by substantial investments in healthcare and government initiativ

The countries with the highest density of hospital beds worldwide include Korea, Japan, Russia, and Germany. Japan has around 12.6 hospital beds per 1,000 population. On the other hand, the United States reported just 2.8 hospital beds per 1,000 population.

Hospital beds in the U.S.

Both the number of hospitals and the number of hospital beds in the U.S. have decreased in recent years. In 2022, there were an estimated 917 thousand hospital beds in the U.S. The largest proportion of hospitals in the U.S. have 500 or more beds, while the second largest proportion of hospitals had between 100 and 199 beds.

Hospital stays in the U.S.

Despite decreasing hospital bed density since 1975, the number of hospital admissions in the U.S. has increased since then, but has dropped since the COVID pandemic. The number of hospital admission per capita differed from state to state with rates highest in the District of Columbia.

Intelligent Hospital System Market Outlook

The global intelligent hospital system market size was valued at approximately USD 15 billion in 2023 and is projected to soar to USD 45 billion by 2032, reflecting a robust CAGR of 12.5% during the forecast period. The surge is driven by advancements in AI and IoT technologies, coupled with an increasing demand for efficient healthcare management systems.

One of the primary growth factors of the intelligent hospital system market is the pressing need to enhance patient care quality amid a burgeoning global population. The integration of advanced technologies such as AI, IoT, and big data analytics into hospital systems helps optimize clinical workflows, improve patient monitoring, and enhance decision-making processes. Furthermore, these technologies facilitate real-time data analytics and predictive maintenance, thus reducing operational costs and improving overall hospital efficiency.

Another significant driver is the increasing incidence of chronic diseases and the aging population, which puts immense pressure on healthcare systems globally. Intelligent hospital systems offer solutions that streamline patient management, reduce hospital readmission rates, and manage chronic diseases more effectively. Additionally, government initiatives and investments in smart healthcare infrastructure play a crucial role in propelling the market forward. Policies promoting digital health and funding for the adoption of intelligent systems in hospitals are expected to create substantial growth opportunities in the coming years.

The growing emphasis on personalized medicine is also contributing to market expansion. Intelligent hospital systems enable the collection and analysis of vast amounts of patient data, which can be used to tailor treatments to individual patients. This personalized approach not only improves patient outcomes but also enhances patient satisfaction. Moreover, the integration of electronic health records (EHRs) with intelligent systems ensures seamless data flow across various healthcare departments, thereby facilitating coordinated care and improving the overall efficiency of healthcare delivery.

The implementation of a High-Acuity Information System is becoming increasingly vital in modern healthcare settings. These systems are designed to manage the complex needs of patients requiring intensive monitoring and care. By integrating real-time data from various sources, such as patient monitors and electronic health records, High-Acuity Information Systems provide healthcare professionals with a comprehensive view of a patient's condition. This integration not only enhances clinical decision-making but also improves patient safety by enabling timely interventions. As healthcare facilities strive to deliver high-quality care, the adoption of High-Acuity Information Systems is expected to rise, further driving the growth of the intelligent hospital system market.

Regionally, North America dominates the intelligent hospital system market, owing to its advanced healthcare infrastructure, high adoption rate of cutting-edge technologies, and substantial investment in healthcare R&D. Europe follows closely, with significant contributions from countries like Germany, France, and the UK. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by increasing healthcare expenditure, rapidly improving healthcare infrastructure, and the adoption of advanced technologies in countries like China, India, and Japan.

Component Analysis

The intelligent hospital system market by component is segmented into hardware, software, and services. The hardware segment includes devices such as sensors, wearables, and medical imaging devices. These hardware components are crucial for collecting real-time data, which is then processed and analyzed to generate actionable insights. The growing demand for advanced diagnostic and monitoring devices is a major factor driving the growth of the hardware segment. Additionally, technological advancements in medical devices are expected to further boost the market during the forecast period.

Software represents a significant portion of the intelligent hospital system market. This segment includes various applications such as clinical decision support systems, EHRs, and medical imaging software. The software segment is witnessing rapid growth due to the increasing need for effici

Supplementary Tables 1–16. Real-world data on diffuse large B-cell lymphoma 2010–2019: usability of large data sets of Finnish hospital data lakes

Digital Hospital Market Outlook

The global digital hospital market size was valued at approximately USD 34.7 billion in 2023 and is projected to reach around USD 89.4 billion by 2032, growing at a CAGR of 11.3% during the forecast period. This remarkable growth is driven by the increasing integration of advanced digital technologies in healthcare settings, aiming to enhance operational efficiency, patient care, and overall hospital management.

One of the primary growth factors for this market is the rising demand for cost-effective healthcare solutions. As global healthcare costs continue to rise, hospitals and clinics are increasingly turning to digital technologies to streamline operations and reduce expenses. The integration of electronic health records (EHRs), telemedicine, and other digital tools not only helps in cutting down costs but also significantly improves the quality of patient care. Additionally, the growing emphasis on patient-centric care models is encouraging healthcare providers to adopt advanced digital solutions that can offer better patient engagement and satisfaction.

Technological advancements in the healthcare sector are another driving force for the digital hospital market. Innovations such as artificial intelligence (AI), machine learning, big data analytics, and IoT are transforming the way hospitals operate. AI and machine learning algorithms, for instance, are being utilized for predictive analytics, enabling healthcare providers to anticipate patient needs and manage resources more efficiently. Moreover, IoT devices are enhancing patient monitoring and care by providing real-time data and enabling remote patient management. These technological advancements are playing a crucial role in propelling the market forward.

The increasing prevalence of chronic diseases and the growing aging population are also significant contributors to the market's growth. Chronic diseases such as diabetes, heart disease, and cancer require continuous monitoring and management, which can be efficiently handled through digital hospital solutions. The aging population, particularly in regions like North America and Europe, is leading to a higher demand for healthcare services. Digital hospitals, with their ability to offer efficient and high-quality care, are well-positioned to meet these rising demands.

Regionally, North America holds the largest share of the digital hospital market, followed by Europe and the Asia Pacific. The high adoption rate of advanced healthcare technologies and the presence of a well-established healthcare infrastructure are major factors driving the market in North America. In contrast, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Rapidly developing healthcare infrastructure, increasing government initiatives, and the growing adoption of digital technologies in countries like China and India are contributing to the market's expansion in this region.

Component Analysis

The component segment of the digital hospital market is categorized into hardware, software, and services. Each of these components plays a vital role in the functioning of a digital hospital. Hardware components include medical devices, computers, servers, and other IT infrastructure that form the backbone of digital hospital operations. The demand for advanced medical devices and IT infrastructure is continually rising due to the need for efficient data management and patient care. High-end servers and storage solutions are particularly crucial for handling large volumes of data generated by digital hospitals.

Software components, on the other hand, encompass electronic health records (EHRs), hospital management systems (HMS), telemedicine software, and other applications that aid in the seamless functioning of hospitals. EHRs are widely adopted due to their ability to store and manage patient data efficiently, thereby improving clinical decision-making. Hospital management systems simplify administrative tasks, while telemedicine software facilitates remote consultations and monitoring, enhancing patient convenience and care quality.

Services include consulting, training, implementation, and maintenance services that ensure the smooth operation of digital hospital systems. Consulting services help healthcare organizations identify and implement the most suitable digital solutions, while training services ensure that hospital staff are well-versed in using these technologies. Implementation services cover the actual deployment of digital systems, and maintenance

The Agency for Healthcare Research and Quality (AHRQ, formerly the Agency for Health Care Policy and Research) maintains the Healthcare Cost and Utilization Project (HCUP). HCUP is a Federal-State-industry partnership to build a standardized, multi-State health data system. AHRQ has taken the lead in developing HCUP databases, Web-based products, and software tools and making them available for restricted access public release.

HCUP comprises a family of administrative longitudinal databases-including State-specific hospital-discharge databases and a national sample of discharges from community hospitals.

HCUP databases contain patient-level information compiled in a uniform format with privacy protections in place. * The Nationwide Inpatient Sample (NIS) includes inpatient data from a national sample (about 20% of U.S. community hospitals) including roughly 7 million discharges from about 1,000 hospitals. It is the largest all-payer inpatient database in the U.S.; data are now available from 1988-1998. The NIS is ideal for developing national estimates, for analyzing national trends, and for research that requires a large sample size. * The State Inpatient Databases (SID) cover individual data sets in community hospitals from 22 participating States that represent more than half of all U.S. hospital discharges. The data have been translated into a uniform format to facilitate cross-State comparisons. The SID are particularly well-suited for policy inquiries unique to a specific State, studies comparing two or more States, market area research, and small area variation analyses.

• The State Ambulatory Surgery Databases (SASD) contain data from ambulatory care encounters in 9 participating States. The SASD capture surgeries performed on the same day in which patients are admitted and released form hospital- affiliated ambulatory surgery sites. The SASD are well suited for research that requires complete enumeration of hospital-based ambulatory surgeries within market areas and States.

• The project's newest restricted access public release is the Kids' Inpatient Database (KID), containing hospital inpatient stays for children 18 years of age and younger. Researchers and policymakers can use the KID to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The KID is the only all-payer inpatient care database for children in the U.S. It contains data from approximately 1.9 million hospital discharges for children. The data are drawn from 22 HCUP 1997 State Inpatient Databases and include a sample of pediatric general discharges from over 2,500 U.S. community hospitals (defined as short-term, non-Federal, general and specialty hospitals, excluding hospital units of other institutions). A key strength of the KID is that the large sample size enables analyses of both common and rare conditions; uncommon treatments, and organ transplantation. The KID also includes charge information on all patients, regardless of payer, including children covered by Medicaid, private insurance, and the uninsured.

  HCUP also contains powerful, user-friendly software that can be used with both HCUP data and with other administrative databases. The AHRQ has developed three powerful software tools Quality Indicators (QIs), Clinical Classification Software (CCS) and HCUPnet. See more on the agency's webpages.

IntroductionIn confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smooth operation of a country or region's healthcare system during a pandemic. However, large makeshift hospitals, such as the Shanghai New International Expo Center (SNIEC) makeshift hospital, which was one of the largest makeshift hospitals in the world, face two major problems: Effective and precise transfer of patients and heterogeneity of the medical care teams.MethodsTo solve these problems, this study presents the medical practices of the SNIEC makeshift hospital in Shanghai, China. The experiences include constructing two groups, developing a medical management protocol, implementing a multi-dimensional management mode to screen patients, transferring them effectively, and achieving homogeneous quality of medical care. To evaluate the medical practice performance of the SNIEC makeshift hospital, 41,941 infected patients were retrospectively reviewed from March 31 to May 23, 2022. Multivariate logistic regression method and a tree-augmented naive (TAN) Bayesian network mode were used.ResultsWe identified that the three most important variables were chronic disease, age, and type of cabin, with importance values of 0.63, 0.15, and 0.11, respectively. The constructed TAN Bayesian network model had good predictive values; the overall correct rates of the model-training dataset partition and test dataset partition were 99.19 and 99.05%, respectively, and the respective values for the area under the receiver operating characteristic curve were 0.939 and 0.957.ConclusionThe medical practice in the SNIEC makeshift hospital was implemented well, had good medical care performance, and could be copied worldwide as a practical intervention to fight the epidemic in China and other developing countries.

About OPCRD

Optimum Patient Care Research Database (OPCRD) is a real-world, longitudinal, research database that provides anonymised data to support scientific, medical, public health and exploratory research. OPCRD is established, funded and maintained by Optimum Patient Care Limited (OPC) – which is a not-for-profit social enterprise that has been providing quality improvement programmes and research support services to general practices across the UK since 2005.

Key Features of OPCRD

OPCRD has been purposefully designed to facilitate real-world data collection and address the growing demand for observational and pragmatic medical research, both in the UK and internationally. Data held in OPCRD is representative of routine clinical care and thus enables the study of ‘real-world’ effectiveness and health care utilisation patterns for chronic health conditions.

OPCRD unique qualities which set it apart from other research data resources: • De-identified electronic medical records of more than 24.9 million patients • OPCRD covers all major UK primary care clinical systems • OPCRD covers approximately 35% of the UK population • One of the biggest primary care research networks in the world, with over 1,175 practices • Linked patient reported outcomes for over 68,000 patients including Covid-19 patient reported data • Linkage to secondary care data sources including Hospital Episode Statistics (HES)

Data Available in OPCRD

OPCRD has received data contributions from over 1,175 practices and currently holds de-identified research ready data for over 24.9 million patients or data subjects. This includes longitudinal primary care patient data and any data relevant to the management of patients in primary care, and thus covers all conditions. The data is derived from both electronic health records (EHR) data and patient reported data from patient questionnaires delivered as part of quality improvement. OPCRD currently holds over 68,000 patient reported questionnaire data on Covid-19, asthma, COPD and rare diseases.

Approvals and Governance

OPCRD has NHS research ethics committee (REC) approval to provide anonymised data for scientific and medical research since 2010, with its most recent approval in 2020 (NHS HRA REC ref: 20/EM/0148). OPCRD is governed by the Anonymised Data Ethics and Protocols Transparency committee (ADEPT). All research conducted using anonymised data from OPCRD must gain prior approval from ADEPT. Proceeds from OPCRD data access fees and detailed feasibility assessments are re-invested into OPC services for the continued free provision of patient quality improvement programmes for contributing practices and patients.

For more information on OPCRD please visit: https://opcrd.co.uk/

Smart Hospital Market size was valued at USD 67.77 Billion in 2024 and is projected to reach 310.17 USD Billion by 2031 growing at a CAGR of 23.10% from 2024 to 2031.

Global Smart Hospital Market Drivers

The market drivers for the Smart Hospital Market can be influenced by various factors. These may include:

Technological breakthroughs: The adoption of smart solutions in hospitals is being driven by the rapid breakthroughs in technology, such as wearables, robotics, artificial intelligence (AI), and the Internet of Things (IoT). Need for Streamlined Operations: To improve efficiency, cut expenses, and streamline operations, hospitals are progressively implementing smart technology. Smart hospital solutions facilitate the automation and optimisation of multiple operations, including resource allocation, inventory management, and patient management. Increasing Healthcare Costs: There is pressure on healthcare providers to cut costs without sacrificing quality of care. By minimising inefficiencies, preventing medical errors, and optimising resource utilisation, smart hospital solutions can lower total healthcare costs. Growing Ageing Population: The need for healthcare services is rising as a result of the global ageing population. More individualised and effective care delivery is made possible by smart hospital technology like telemedicine and remote monitoring, especially for older patients with chronic illnesses. Emphasis on Patient-Centric Care: Improving patient experiences and offering patient-centric care are becoming more and more important in healthcare facilities. Electronic health records (EHRs), patient portals, and remote monitoring devices are examples of smart hospital solutions that enable people to take an active role in their healthcare management and improve communication between patients and healthcare professionals. Government Regulations and Initiatives: The adoption of smart hospital solutions is being driven by government regulations and initiatives that aim to improve healthcare infrastructure and promote healthcare digitization. Additionally, hospitals are being encouraged to invest in smart technologies for compliance by regulatory demands such as those pertaining to electronic medical records and patient data protection. Impact of the COVID-19 Pandemic: In order to facilitate contactless healthcare delivery, telemedicine consultations, and remote patient monitoring, smart hospital solutions have become increasingly popular. This has helped the market for smart hospitals develop even more.

*Standardized units.Characteristics of the top 50 Cancer Hospitals, as ranked by the US News and World Report.