Healthcare Provider Population Health Management Software Market Valuation – 2024-2031

Healthcare Provider Population Health Management Software Market was valued at USD 35.01 Billion in 2024 and is projected to reach USD 126.14 Billion by 2031, growing at a CAGR of 17.4% from 2024 to 2031.

Healthcare Provider Population Health Management Software Market Drivers

Rising healthcare costs: Healthcare providers are under increasing pressure to reduce costs while improving quality of care. Population health management software helps them achieve these goals by identifying and addressing health risks in patient populations.

Shift to value-based care: The shift from fee-for-service to value-based care models incentivizes healthcare providers to improve patient outcomes and reduce costs. Population health management software is essential for this transition.

Increasing chronic disease burden: The growing prevalence of chronic diseases, such as diabetes and heart disease, creates a need for proactive management and prevention. Population health management software helps healthcare providers identify and address these conditions.

Healthcare Provider Population Health Management Software Market Restraints

Data privacy and security concerns: The collection and analysis of patient data raise concerns about privacy and security. Healthcare providers must ensure that patient data is protected.

Interoperability challenges: The lack of interoperability between different healthcare systems and data sources can hinder the effective use of population health management software.

Healthcare fraud is considered a challenge for many societies. Health care funding that could be spent on medicine, care for the elderly, or emergency room visits is instead lost to fraudulent activities by materialistic practitioners or patients. With rising healthcare costs, healthcare fraud is a major contributor to these increasing healthcare costs.

Try out various unsupervised techniques to find the anomalies in the data.

Detailed Data File:

The following variables are included in the detailed Physician and Other Supplier data file (see Appendix A for a condensed version of variables included)).

npi – National Provider Identifier (NPI) for the performing provider on the claim. The provider NPI is the numeric identifier registered in NPPES.

nppes_provider_last_org_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s last name. When the provider is registered as an organization (entity type code = ‘O’), this is the organization's name.

nppes_provider_first_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s first name. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_mi – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s middle initial. When the provider is registered as an organization (entity type code= ‘O’), this will be blank.

nppes_credentials – When the provider is registered in NPPES as an individual (entity type code=’I’), these are the provider’s credentials. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_gender – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s gender. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_entity_code – Type of entity reported in NPPES. An entity code of ‘I’ identifies providers registered as individuals and an entity type code of ‘O’ identifies providers registered as organizations.

nppes_provider_street1 – The first line of the provider’s street address, as reported in NPPES.

nppes_provider_street – The second line of the provider’s street address, as reported in NPPES.

nppes_provider_city – The city where the provider is located, as reported in NPPES.

nppes_provider_zip – The provider’s zip code, as reported in NPPES.

nppes_provider_state – The state where the provider is located, as reported in NPPES. The fifty U.S. states and the District of Columbia are reported by the state postal abbreviation. The following values are used for all other areas:

'XX' = 'Unknown' 'AA' = 'Armed Forces Central/South America' 'AE' = 'Armed Forces Europe' 'AP' = 'Armed Forces Pacific' 'AS' = 'American Samoa' 'GU' = 'Guam' 'MP' = 'North Mariana Islands' 'PR' = 'Puerto Rico' 'VI' = 'Virgin Islands' 'ZZ' = 'Foreign Country'

nppes_provider_country – The country where the provider is located, as reported in NPPES. The country code will be ‘US’ for any state or U.S. possession. For foreign countries (i.e., state values of ‘ZZ’), the provider country values include the following: AE=United Arab Emirates IT=Italy AG=Antigua JO= Jordan AR=Argentina JP=Japan AU=Australia KR=Korea BO=Bolivia KW=Kuwait BR=Brazil KY=Cayman Islands CA=Canada LB=Lebanon CH=Switzerland MX=Mexico CN=China NL=Netherlands CO=Colombia NO=Norway DE= Germany NZ=New Zealand ES= Spain PA=Panama FR=France PK=Pakistan GB=Great Britain RW=Rwanda GR=Greece SA=Saudi Arabia HU= Hungary SY=Syria IL= Israel TH=Thailand IN=India TR=Turkey IS= Iceland VE=Venezuela

provider_type – Derived from the provider specialty code reported on the claim.

medicare_participation_indicator – Identifies whether the provider participates in Medicare and/or accepts the assigned assignment of Medicare allowed amounts.

place_of_service – Identifies whether the place of service submitted on the claims is a facility (value of ‘F’) or non-facility (value of ‘O’). Non-facility is generally an office setting; however other entities are included in non-facility.

hcpcs_code – HCPCS code used to identify the specific medical service furnished by the provider.

hcpcs_description – Description of the HCPCS code for the specific medical service furnished by the provider.

hcpcs_drug_indicator –Identifies whether the HCPCS code for the specific service furnished by the provider is an HCPCS listed on the Medicare Part B Drug Average Sales Price (ASP) File.

line_srvc_cnt – Number of services provided; note that the metrics used to count the number provided can vary from service to service.

bene_unique_cnt – Number of distinct Medicare beneficiaries rec...

Population Health Management Market size was valued at USD 26.79 Billion in 2024 and is projected to reach USD 77.65 Billion by 2032, growing at a CAGR of 14.23% from 2026 to 2032.Shift Toward Value Based Care: The transition from a traditional fee for service model to value based care is a primary catalyst for the PHM market. Under the old model, healthcare providers were reimbursed based on the volume of services rendered, which often led to unnecessary procedures and a lack of care coordination. Rising Healthcare Costs: Healthcare costs are spiraling globally due to factors like an aging population, the high price of new technologies, and the growing prevalence of chronic diseases. This financial pressure is compelling healthcare organizations to seek innovative ways to improve efficiency and manage resources more effectively.

Information on provider-level measure rates for the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator 11 (PSI-11) Postoperative Respiratory Failure measure.The Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator 11 (PSI-11) Measure Rates dataset provides information on provider-level measure rates regarding one preventable complication (postoperative respiratory failure) for Medicare fee-for-service discharges. The PSI-11 measure data is solely reported for providers’ information and quality improvement purposes and are not a part of the Deficit Reduction Act (DRA) Hospital-Acquired Condition (HAC) Payment Provision or HAC Reduction Program.Q: What is the history of the PSI-11 measure reporting?In August 2015, CMS calculated and publicly reported the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator (PSI) 11 – Postoperative Respiratory Failure Rate on data.cms.gov. CMS publicly reported the same PSI-11 measure again in August 2016. CMS reports the AHRQ PSI-11 – Postoperative Respiratory Failure Rate measure for information and quality improvement purposes only; PSI-11 is not a part of the Deficit Reduction Act (DRA) Hospital-Acquired Condition (HAC) Payment Provision or HAC Reduction Program.Q: How do the PSI-11 results being posted in August 2016 differ from the PSI-11 results from August 2015?CMS made the following changes since the previous reporting of the PSI-11 measure:Updated time period for measures calculation — CMS updated the time period used for the PSI-11 measure calculations to include discharges from July 1, 2013 through June 30, 2015 (as opposed to July 1, 2011 through June 30, 2013).Updated and recalibrated AHRQ PSI software for PSI-11 — CMS calculated the PSI-11 measure using recalibrated version 5.0.1 of the AHRQ PSI software, as opposed to version 4.5a. In general, CMS recalibrated the risk-adjustment coefficients, signal variance, smoothing target, and composite weights based on the Medicare Fee-for-Service (FFS) population rather than the Healthcare Cost and Utilization Project (HCUP) population.Inclusion of Maryland hospitals – CMS will include Maryland hospitals in the calculation of the PSI-11 measure for the first time in August 2016 because Maryland hospitals were required to start reporting POA Indicators, a field on an inpatient claim necessary for the PSI-11 measure calculations, as of October 1, 2013. Q: Why is CMS reporting the PSI-11 measure rate?In addition to researcher and stakeholder interest, CMS is publicly reporting the PSI-11 measure rate to identify complications and undesirable conditions that patients experience in hospital settings which can reasonably be prevented by changes at the hospital level. Improving patient safety is one of the ultimate goals of quality improvement. The PSI-11 measure remains an important aspect of CMS’s commitment to patient safety.Q: Which hospitals are included in the PSI-11 measure calculations?The PSI-11 measure depends on complete and accurate coding of POA Indicator fields. Hospitals participating in the IPPS program and Maryland hospitals must submit complete POA coding, although other types of hospitals can and will report these codes. To avoid any bias against exempt hospitals that are not reporting POA indicators, the PSI-11 measure is only calculated for IPPS and Maryland hospitals.A list of hospital types exempt from POA reporting is provided on the CMS Hospital-Acquired Conditions webpage under the link for Affected Hospitals located at the following website: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/HospitalAcqCond/AffectedHospitals.htmlQ: How is the PSI-11 measure rate calculated?CMS calculates the PSI-11 measure rate using claims for Medicare Fee-for-Service (FFS) discharges.The PSI-11 measure rate is reported as a smoothed rate. The measure uses the count of actual occurrences identified at a hospital (numerator) divided by the eligible number of discharges at that hospital (denominator). This ratio is then risk-adjusted to account for the hospital’s case mix and reliability-adjusted (or “smoothed”) to account for statistical uncertainty.Q: Is the PSI-11 measure adjusted for our hospital’s patient case-mix?The PSI-11 measure is risk and reliability-adjusted, according to AHRQ’s specifications.Q: How ar

Population Health Management Software Market Outlook

The Population Health Management (PHM) Software Market size is poised to experience substantial growth over the forecast period, increasing from an estimated $XX billion in 2023 to $XX billion by 2032, with a compound annual growth rate (CAGR) of XX%. This growth is largely driven by the rising demand for effective healthcare management solutions, as healthcare providers and payers aim to enhance patient outcomes and reduce costs through data-driven decisions. The increasing prevalence of chronic diseases and the need for government policies that encourage the adoption of healthcare IT solutions are also significant catalysts that are contributing to the robust expansion of this market. As healthcare shifts towards a value-based care system, the importance of PHM software continues to rise, supporting healthcare organizations in managing patient populations more effectively.

One of the primary growth factors for the PHM software market is the increasing emphasis on value-based care, which prioritizes patient outcomes rather than the volume of services provided. This shift requires healthcare providers to adopt comprehensive population health strategies that not only treat illnesses but also prevent them. PHM software plays a crucial role in this transition by enabling healthcare organizations to collect and analyze vast amounts of patient data, identify at-risk populations, and implement preventive care measures. Additionally, the software's ability to integrate with various healthcare systems, including electronic health records (EHRs), enhances its utility and appeal, fostering its widespread adoption. The emphasis on improving patient engagement and care coordination further propels the market forward.

The growing prevalence of chronic diseases, such as diabetes, cardiovascular disorders, and respiratory illnesses, is another significant factor driving the demand for population health management solutions. As these conditions require continuous monitoring and long-term management, PHM software offers healthcare providers the tools necessary to deliver personalized care plans and track patient progress over time. This capability not only improves patient outcomes but also reduces healthcare costs by minimizing hospital readmissions and emergency room visits. Moreover, the software's analytical capabilities enable healthcare organizations to identify trends and patterns in patient data, allowing for more effective resource allocation and strategic planning in managing chronic disease populations.

Government initiatives and regulatory frameworks that promote the adoption of healthcare IT solutions are also pivotal in driving the PHM software market's growth. Many governments worldwide are implementing programs and policies that encourage healthcare organizations to invest in advanced technologies aimed at improving patient care and operational efficiency. For instance, initiatives like the Health Information Technology for Economic and Clinical Health (HITECH) Act in the United States provide financial incentives for healthcare providers to adopt electronic health records and other health IT solutions, indirectly boosting the demand for PHM software. Additionally, regulatory bodies are establishing standards and guidelines to ensure data security and privacy, which further facilitates the adoption of these solutions.

Regionally, North America is anticipated to maintain its dominance in the PHM software market, driven by advanced healthcare infrastructure, significant investments in healthcare IT, and supportive regulatory policies. In contrast, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, attributed to increasing healthcare expenditure, the rising prevalence of chronic diseases, and the rapid adoption of digital healthcare technologies. European countries are also witnessing a steady increase in the adoption of PHM software, supported by government initiatives aimed at improving healthcare delivery and patient outcomes. Meanwhile, Latin America and the Middle East & Africa regions are gradually embracing population health management strategies, driven by the need to address healthcare challenges and improve overall population health.

Component Analysis

The PHM software market is segmented by component into software and services, each playing a critical role in the overall market dynamics. The software segment encompasses the various digital solutions used for data integration, analytics, patient engagement, and care coordination. These solutions are essential for healthcare provide

Age-adjustment mortality rates are rates of deaths that are computed using a statistical method to create a metric based on the true death rate so that it can be compared over time for a single population (i.e. comparing 2006-2008 to 2010-2012), as well as enable comparisons across different populations with possibly different age distributions in their populations (i.e. comparing Hispanic residents to Asian residents). Age adjustment methods applied to Montgomery County rates are consistent with US Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) as well as Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). PHS Planning and Epidemiology receives an annual data file of Montgomery County resident deaths registered with Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). Using SAS analytic software, MCDHHS standardizes, aggregates, and calculates age-adjusted rates for each of the leading causes of death category consistent with state and national methods and by subgroups based on age, gender, race, and ethnicity combinations. Data are released in compliance with Data Use Agreements between DHMH VSA and MCDHHS. This dataset will be updated Annually.

Adjusted rate ratios for the association between population characteristics and provider spatial accessibility.

Share of adults who reported the following financial conditions, 2021. Notes: *All differences between "No medical debt" and "Has medical debt" significant at p < .05.

Notes: Price and quantity indices are for health services consumption. Index numbers 2017=100

Demographic trends play a major role in shaping the healthcare landscape, as economic factors and an aging population contribute to fast-rising healthcare spending. While consumers are spending more on healthcare services in the US, healthcare providers are confronting complex challenges related to labor, competition and tech advances. COVID-19 exposed healthcare and social assistance providers to unprecedented financial and operating pressures, with the lasting impacts still shaping every corner of the sector in 2025. Providers continue to grapple with workforce shortages intensified by the pandemic, resulting in ongoing staffing and recruitment challenges that pressure wage growth and new strategies to recruit and retain. At the same time, consolidation activity is reshaping the landscape, with more patients than ever receiving care from massive, integrated health systems rather than independent ones. Meanwhile, social assistance providers are finding it difficult to meet rising demand for services like food banks and emergency shelters. Despite this challenging operating environment, revenue has been expanding at a CAGR of 4.0% to an estimated $4.3 trillion over the past five years, with revenue rising an expected 2.3% in 2025. Healthcare and social assistance providers are struggling to address staffing challenges. The pandemic exacerbated existing staffing shortages, as the physical and mental toll of the pandemic pushed some to leave the sector entirely. Persistent labor shortages jeopardize healthcare and social assistance providers' ability to address demand, creating widespread staff burnout, high turnover rates and wage inflation. While the health sector labor market began stabilizing in 2024, alleviating wage pressures, an undersized workforce still leaves hundreds of thousands of jobs open. Staff shortages have been a driver of AI adoption in the health sector, as organizations adopt tech solutions to maintain care quality and efficiency with fewer personnel. Automating time- and cost-intensive administrative task helps organizations cope with labor shortages, but also enhances operating efficiency and patient outcomes amid workforce gaps. Demographic trends will remain the driving force behind rising healthcare spending moving forward. However, increasing demand and elevated costs will pressure healthcare and social assistance providers to shift how they operate. For example, investments in digital tools, including AI, and telehealth will accelerate because of their ability to lower costs, increase capacity and improve patient outcomes. As this occurs, cybersecurity will become a core priority, as health systems must mitigate the impact of increasingly disruptive and sophisticated cyberattacks. The sector will also face significant challenges from Medicaid cuts resulting from the OBBBA, as estimates suggest that nearly 17.0 million people will lose health coverage between 2026 and 2034. This substantial loss of coverage is likely to strain providers, particularly those serving large Medicaid and uninsured populations, creating new financial pressures. These dynamics will reinforce and accelerate the ongoing consolidation activity, as providers increasingly seek mergers or acquisitions to access resources, achieve operating efficiencies and ensure stability. In all, sector revenue will grow at a CAGR 3.4% to reach an estimated $5.0 trillion over the next five years.

Value-Based Care Platforms Market Outlook

According to our latest research, the global value-based care platforms market size reached USD 5.7 billion in 2024, reflecting robust adoption across healthcare systems striving for improved outcomes and cost efficiencies. The market is expected to grow at a CAGR of 15.1% from 2025 to 2033, reaching a projected value of USD 20.7 billion by 2033. This rapid expansion is driven by the increasing transition from fee-for-service to value-based care models, as healthcare providers and payers worldwide seek to enhance patient outcomes while controlling escalating healthcare costs.

The primary growth factor for the value-based care platforms market is the global shift in healthcare reimbursement strategies. Governments and private payers are increasingly incentivizing providers to adopt value-based models, which reward improved patient outcomes rather than the volume of services delivered. This shift has necessitated the adoption of advanced digital platforms capable of aggregating and analyzing patient data, supporting care coordination, and enabling performance-based payments. Additionally, the proliferation of chronic diseases and the aging population have heightened the need for efficient, outcome-driven care delivery, further propelling demand for sophisticated value-based care solutions.

A second significant driver is the surge in digital health investments and technological advancements. The integration of artificial intelligence, machine learning, and robust analytics within value-based care platforms enables real-time monitoring of patient health metrics, predictive risk assessment, and personalized care planning. These innovations not only streamline clinical workflows but also empower providers to proactively manage high-risk populations, reduce hospital readmissions, and improve overall care quality. As interoperability standards improve, seamless data exchange across disparate health information systems is becoming a reality, making value-based care platforms more effective and attractive to healthcare organizations of all sizes.

Regulatory developments and policy initiatives are also fueling market growth. In regions such as North America and Europe, government mandates and incentive programs are accelerating the adoption of value-based care. For example, the United States’ Centers for Medicare & Medicaid Services (CMS) continue to expand alternative payment models, while the European Union is investing in digital health infrastructure to support coordinated care. These policy efforts, combined with the rising demand for cost transparency and accountability, are compelling healthcare stakeholders to invest in platforms that facilitate value-based reimbursement and population health management.

From a regional perspective, North America currently dominates the value-based care platforms market, accounting for over 45% of global revenue in 2024. This leadership is attributed to the region’s advanced healthcare IT infrastructure, favorable reimbursement landscape, and early adoption of digital health technologies. However, Asia Pacific is emerging as the fastest-growing region, with a forecasted CAGR of 18.2% through 2033, fueled by healthcare reforms, increased government spending, and growing awareness of value-based care benefits. Europe also holds a substantial share, propelled by cross-border health initiatives and a strong focus on patient-centered care.

Component Analysis

The value-based care platforms market by component is segmented into software and services. Software solutions form the backbone of value-based care, providing essential functionalities such as patient data aggregation, risk stratification, care coordination, and analytics. These platforms enable healthcare providers to transition from siloed, episodic care to a more integrated, outcome-focused approach. The demand for comprehensive software suites is escalating as providers seek to comply with regulatory requirements, streamline workflows, and track performance metrics crucial for value-based reimbursement.

On the other hand, services play a pivotal role in ensuring the successful deployment and optimization of value-based care platforms. These services include consulting, implementation, training, support, and managed services. As value-based care adoptio

Dataset

This dataset was created by Rajratna Tayade

Released under CC0: Public Domain

Contents

Commercial Medical Insurance Market Outlook

The global commercial medical insurance market size was valued at approximately USD 1.8 trillion in 2023 and is projected to reach around USD 3.6 trillion by 2032, growing at a compounded annual growth rate (CAGR) of 8.1% during the forecast period. This substantial growth can be attributed to the increasing healthcare costs, rising awareness about the importance of health insurance, and the expanding corporate sector globally. The market is also driven by the increasing prevalence of chronic diseases and the aging population, which further necessitate the need for comprehensive medical insurance coverage.

One of the primary growth factors for the commercial medical insurance market is the escalating cost of healthcare services globally. As medical treatments and healthcare services become more advanced, the costs associated with these services have significantly increased. This trend has made it imperative for individuals and corporations to invest in medical insurance to mitigate financial risks associated with unexpected medical expenses. Additionally, technological advancements in the healthcare sector have led to the development of more sophisticated and expensive treatment options, further propelling the demand for medical insurance.

Another significant factor contributing to the market growth is the rising awareness about the benefits of medical insurance among the general population. With the increasing dissemination of information and educational campaigns by governments and private entities, more people are becoming aware of the importance of having medical insurance. This awareness drives the adoption of medical insurance policies, as individuals and families seek to protect themselves against the high costs of medical care. Moreover, the growing middle-class population in emerging economies is also contributing to the increased uptake of commercial medical insurance.

The expanding corporate sector globally is another crucial driver for the commercial medical insurance market. Many corporations offer health insurance as part of their employee benefits package, which helps attract and retain talent. As businesses become more competitive, providing comprehensive health insurance becomes a vital tool for employee satisfaction and productivity. Furthermore, with the rise of multinational companies and the increasing trend of globalization, there is a growing demand for comprehensive health insurance policies that provide coverage across different countries.

Health Insurance plays a pivotal role in mitigating the financial risks associated with medical expenses, especially in the context of rising healthcare costs. As healthcare services become more sophisticated and expensive, the demand for health insurance has surged, providing individuals and families with a safety net against unforeseen medical events. Health insurance not only covers the costs of medical treatments but also promotes preventive care, encouraging policyholders to engage in regular health check-ups and wellness programs. This proactive approach to health management is crucial in reducing the incidence of chronic diseases and improving overall health outcomes. Furthermore, health insurance policies often come with additional benefits such as access to a wide network of healthcare providers, discounted services, and coverage for alternative therapies, making them an essential component of personal financial planning.

Regionally, North America holds a significant share in the commercial medical insurance market, driven by the well-established healthcare infrastructure, high healthcare costs, and high awareness levels among the population. Europe also represents a substantial market, supported by robust health insurance regulations and widespread insurance coverage. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the rapidly growing middle-class population, increasing healthcare costs, and rising awareness about health insurance. Latin America and the Middle East & Africa are also anticipated to experience steady growth, driven by improving economic conditions and rising healthcare expenditures.

Type Analysis

The commercial medical insurance market is segmented by type into group insurance and individual insurance. Group insurance refers to po

The Physician and Other Supplier PUF contains information on utilization, payment (allowed amount and Medicare payment), and submitted charges organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System (HCPCS) code, and place of service. This PUF is based on information from CMS administrative claims data for Medicare beneficiaries enrolled in the fee-for-service program available from the CMS Chronic Condition Data Warehouse (www.ccwdata.org). The data in the Physician and Other Supplier PUF covers calendar year 2014 and contains 100% final-action physician/supplier Part B non-institutional line items for the Medicare fee-for-service population.

Notes: Health spending is shown in terms of both nominal dollar values (not inflation-adjusted) and constant 2023 dollars (inflation-adjusted based on the personal consumption expenditures (PCE) annual index).

The highdose quadrivalent influenza vaccine (QIVHD) has shown improved protection against influenza and its complications in older adults. We aimed to evaluate the costeffectiveness of QIVHD compared with QIVSD among Korean adults aged ≥ 65 years in reducing influenzarelated disease burden. We evaluated the 2016/2017 and 2017/2018 seasons and their average values using a static decision tree model. The difference in efficacy between standard-dose (SD) and high-dose (HD) was calculated based on the results of a clinical trial comparing Fluzone® High-Dose Vaccine and Fluzone® Vaccine in older adults. Incremental cost-effectiveness ratios (ICERs) were assessed from the healthcare system perspective. A discount rate of 4.5% was applied to life-year-gained (LYG) values and utilities. We performed deterministic and probabilistic sensitivity analyses to account for both epidemiological and economic sources of uncertainty. In the analysis of the 2017/2018 season, the QIV-HD strategy generated an excess of 0.00182 life-years (Lys)/person and 0.003953 quality-adjusted life-years (QALYs)/person compared with QIV-SD. The ICER was 6,467.56 United States Dollars (USD)/QALY. In the analysis from the 2016/2017 season, QIV-HD caused a surplus of 0.00117 Lys/person and 0.003272 QALYs/person compared with QIV-SD. ICER was 7,902.46 USD /QALY. From the average data of the 2016/2017 and 2017/2018 seasons, an excess of 0.00147 Lys/person and 0.003561 QALYs/person were generated using QIV-HD compared with QIV-SD, while the ICER was 7,190.44 USD /QALY. From the healthcare system perspective, QIV-HD was a more cost-effective vaccination option in reducing influenza-related disease burden and healthcare costs in Koreans aged ≥ 65 years compared with QIV-SD.

Notes: Differences between Black and White people as of 2023 were statistically significant at the p<0.05 level. Includes individuals ages 0 to 64. AIAN refers to American Indian or Alaska Native. NHPI refers to Native Hawaiian or Pacific Islander. Persons of Hispanic origin may be of any race but are categorized as Hispanic for this analysis; other groups are non-Hispanic. 2020 data excluded because the American Community Survey did not release the 1-year estimates for 2020 due to significant disruptions to data collection brought on by the coronavirus pandemic.

The Hospice Utilization and Payment Public Use File provides information on services provided to Medicare beneficiaries by hospice providers. The Hospice PUF contains information on utilization, payment (Medicare payment and standard payment), submitted charges, primary diagnoses, sites of service, and hospice beneficiary demographics organized by CMS Certification Number 6-digit provider identification number, and state. This PUF is based on information from CMSs Chronic Conditions Data Warehouse (CCW) data files. The data in the Hospice PUF covers calendar year 2014 and contains 100 percent final-action i.e., all claim adjustments have been resolved, hospice claims for the Medicare population including beneficiaries enrolled in a Medicare Advantage plan.
Although the Hospice PUF has a wealth of payment and utilization information about hospice services, the data set also has a number of limitations. The information presented in this file does not indicate the quality of care provided by individual hospice providers. The file only contains cost and utilization information. Additionally, the data are not risk adjusted and thus do not account for differences in patient populations. For additional limitations, please review the methodology document available below.

This dataset represents the counts of those individuals who have been determined to have a share of cost (SOC) obligation, which is the monthly amount of medical expenses they must incur before they are eligible to receive Medi-Cal benefits. The dataset includes individuals who have a met or unmet monthly SOC obligation. Individuals who have not met their monthly SOC obligation are not eligible for Medi-Cal. SOC obligations are calculated during the eligibility determination process based on household income.