scholarly journals A Patient-Centered Framework for Measuring the Economic Value of the Clinical Benefits of Digital Health Apps: Theoretical Modeling

10.2196/18812 ◽  
2020 ◽  
Vol 7 (10) ◽  
pp. e18812
Author(s):  
Adam Powell ◽  
John Torous
Keyword(s):  
Digital Health ◽  
Economic Value ◽  
Research Priorities ◽  
Health Impact ◽  
The United States ◽  
Quality Adjusted Life Year ◽  
Patient Centered ◽  
Health Apps ◽  
Clinical Benefits ◽  
Country Specific

Background As digital health tools such as smartphone apps evolve and enter clinical use, questions regarding their value must be addressed. Although there are scarce generalizable data on the value of health apps given their nascency and diverse use cases, it is possible to estimate the economic value of the clinical improvement they bring to patients using a quality-adjusted life-year (QALY)-based approach and generalized values from existing literature. Objective This paper aims to provide a patient-centered framework for assessing the economic value of the clinical benefits delivered by digital health apps. Methods We proposed a model based upon 5 levers: country-specific monetary value of a QALY, QALYs lost due to the condition, engagement rate of app users, average effect size of the app’s health impact, and duration of the app’s impact before remission. Results Using 2 digital health apps from the United States and United Kingdom as examples, we explored how this model could generate country-specific estimates of the economic value of the clinical benefits of health apps. Conclusions This new framework can help drive research priorities for digital health by elucidating the factors that influence the economic value.

scholarly journals A Patient-Centered Framework for Measuring the Economic Value of the Clinical Benefits of Digital Health Apps: Theoretical Modeling (Preprint)

2020 ◽  
Author(s):  
Adam Powell ◽  
John Torous
Keyword(s):  
Digital Health ◽  
Economic Value ◽  
Research Priorities ◽  
Health Impact ◽  
The United States ◽  
Quality Adjusted Life Year ◽  
Patient Centered ◽  
Health Apps ◽  
Clinical Benefits ◽  
Country Specific

BACKGROUND As digital health tools such as smartphone apps evolve and enter clinical use, questions regarding their value must be addressed. Although there are scarce generalizable data on the value of health apps given their nascency and diverse use cases, it is possible to estimate the economic value of the clinical improvement they bring to patients using a quality-adjusted life-year (QALY)-based approach and generalized values from existing literature. OBJECTIVE This paper aims to provide a patient-centered framework for assessing the economic value of the clinical benefits delivered by digital health apps. METHODS We proposed a model based upon 5 levers: country-specific monetary value of a QALY, QALYs lost due to the condition, engagement rate of app users, average effect size of the app’s health impact, and duration of the app’s impact before remission. RESULTS Using 2 digital health apps from the United States and United Kingdom as examples, we explored how this model could generate country-specific estimates of the economic value of the clinical benefits of health apps. CONCLUSIONS This new framework can help drive research priorities for digital health by elucidating the factors that influence the economic value.

scholarly journals DiabetesSistersVoices: Virtual Patient Community to Identify Research Priorities for Women Living With Diabetes (Preprint)

2019 ◽  
Author(s):  
Peijin Han ◽  
Wanda Nicholson ◽  
Anna Norton ◽  
Karen Graffeo ◽  
Richard Singerman ◽  
...  
Keyword(s):  
Women Of Color ◽  
Virtual Community ◽  
Research Priorities ◽  
The United States ◽  
Virtual Patient ◽  
Diabetes Research ◽  
Patient Centered ◽  
Web Based ◽  
Use Of Social Media

BACKGROUND Women with or at high risk of diabetes have unique health concerns across their life course. Effective methods are needed to engage women living with diabetes to develop and carry out a patient-centered research agenda. OBJECTIVE This study aimed to (1) describe the creation of DiabetesSistersVoices, a virtual patient community for women living with and at risk for diabetes and (2) assess the feasibility and acceptability of DiabetesSistersVoices for engaging women in talking about their experiences, health care, and research priorities. METHODS We partnered with a national advocacy organization to create DiabetesSistersVoices and to develop recruitment strategies, which included use of social media, Web-based newsletters, and weblinks through partnering organizations. Study inclusion criteria were as follows: Being a woman aged ≥18 years, residing in the United States, and self-reporting a diagnosis of diabetes or risk of diabetes. Eligible participants were given access to DiabetesSistersVoices and completed online surveys at enrollment and 6 months. We assessed trends in participants’ activities, including posting questions, sharing experiences about living with diabetes, and searching for posted resources. RESULTS We enrolled 332 women (white: 86.5%; type 1 diabetes: 76.2%; median age: 51 years [interquartile range: 31 to 59 years]) over 8 months. Most (41.6%, 138/332) were classified as being active users (ie, posting) of the virtual community, 36.1% (120/332) as observers (ie, logged in but no posts), and 22.3% (74/332) as never users (ie, completed baseline surveys but then never logged in). Online activities were constant during the study, although participants had the highest website usage during the first 10 weeks after their enrollment. CONCLUSIONS We demonstrated the feasibility and acceptability of an online patient community for women living with diabetes by showing durability of recruitment and online usage over 6 months of testing. Next steps are to address barriers to joining a virtual patient community for women of color and women with type 2 diabetes to enhance inclusiveness and gain diverse perspectives to inform diabetes research.

scholarly journals Assessing the Food and Drug Administration’s Risk-Based Framework for Software Precertification With Top Health Apps in the United States: Quality Improvement Study

10.2196/20482 ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. e20482
Author(s):  
Noy Alon ◽  
Ariel Dora Stern ◽  
John Torous
Keyword(s):  
United States ◽  
Quality Improvement ◽  
Health Information ◽  
Digital Health ◽  
The United States ◽  
Health Condition ◽  
User Engagement ◽  
Regulatory Review ◽  
Regulatory Guidance ◽  
Health Apps

Background As the development of mobile health apps continues to accelerate, the need to implement a framework that can standardize the categorization of these apps to allow for efficient yet robust regulation is growing. However, regulators and researchers are faced with numerous challenges, as apps have a wide variety of features, constant updates, and fluid use cases for consumers. As past regulatory efforts have failed to match the rapid innovation of these apps, the US Food and Drug Administration (FDA) has proposed that the Software Precertification (Pre-Cert) Program and a new risk-based framework could be the solution. Objective This study aims to determine whether the risk-based framework proposed by the FDA’s Pre-Cert Program could standardize categorization of top health apps in the United States. Methods In this quality improvement study during summer 2019, the top 10 apps for 6 disease conditions (addiction, anxiety, depression, diabetes, high blood pressure, and schizophrenia) in Apple iTunes and Android Google Play Store in the United States were classified using the FDA’s risk-based framework. Data on the presence of well-defined app features, user engagement methods, popularity metrics, medical claims, and scientific backing were collected. Results The FDA’s risk-based framework classifies an app’s risk by the disease condition it targets and what information that app provides. Of the 120 apps tested, 95 apps were categorized as targeting a nonserious health condition, whereas only 7 were categorized as targeting a serious condition and 18 were categorized as targeting a critical condition. As the majority of apps targeted a nonserious condition, their risk categorization was largely determined by the information they provided. The apps that were assessed as not requiring FDA review were more likely to be associated with the integration of external devices than those assessed as requiring FDA review (15/58, 26% vs 5/62, 8%; P=.03) and health information collection (24/58, 41% vs 9/62, 15%; P=.008). Apps exempt from the review were less likely to offer health information (25/58, 43% vs 45/62, 72%; P<.001), to connect users with professional care (7/58, 12% vs 14/62, 23%; P=.04), and to include an intervention (8/58, 14% vs 35/62, 55%; P<.001). Conclusions The FDA’s risk-based framework has the potential to improve the efficiency of the regulatory review process for health apps. However, we were unable to identify a standard measure that differentiated apps requiring regulatory review from those that would not. Apps exempt from the review also carried concerns regarding privacy and data security. Before the framework is used to assess the need for a formal review of digital health tools, further research and regulatory guidance are needed to ensure that the Pre-Cert Program operates in the greatest interest of public health.

Digital Health for the Opioid Crisis: Funding the Future (Preprint)

2020 ◽  
Author(s):  
En-Ju Deborah Lin ◽  
Madeleine Schroeder ◽  
Yungui Huang ◽  
Simon Lin
Keyword(s):  
New Technologies ◽  
Gradual Increase ◽  
Digital Health ◽  
Research Priorities ◽  
Federal Funding ◽  
The United States ◽  
Opioid Use ◽  
Federal Research Funding ◽  
Opioid Crisis ◽  
Federal Research

UNSTRUCTURED The opioid crisis is ravaging economies and communities across the United States. Technology has the potential to end this crisis. Digital health offers new ways to reach, diagnose, and treat individuals with opioid use disorders. Federal research funding tends to reflect the nation’s research priorities and shape the direction of innovation. We reviewed funded projects by the National Institute on Drug Abuse (NIDA) from 2013 to 2017, a period leading to the substantial increase in federal funding and the launch of the HEAL (Helping End Addiction Long-TermSM) initiative in 2018. We presented our viewpoint of the research landscape of the digital health development for the opioid crisis. Overall, there was a gradual increase in NIDA grant funding for technology in the opioid crisis and the percentage of NIDA technology awards funding new projects has nearly doubled. More specifically, we discuss the types of applications and potential challenges in five emerging technology categories: electronic health, mobile health, virtual reality, artificial intelligence, and biosensor. Diversification of funding in these categories offers the promise of more innovation in new technologies to combat the opioid epidemic.

scholarly journals Assessing the Food and Drug Administration’s Risk-Based Framework for Software Precertification With Top Health Apps in the United States: Quality Improvement Study (Preprint)

2020 ◽  
Author(s):  
Noy Alon ◽  
Ariel Dora Stern ◽  
John Torous
Keyword(s):  
United States ◽  
Quality Improvement ◽  
Health Information ◽  
Digital Health ◽  
The United States ◽  
Health Condition ◽  
User Engagement ◽  
Regulatory Review ◽  
Regulatory Guidance ◽  
Health Apps

BACKGROUND As the development of mobile health apps continues to accelerate, the need to implement a framework that can standardize the categorization of these apps to allow for efficient yet robust regulation is growing. However, regulators and researchers are faced with numerous challenges, as apps have a wide variety of features, constant updates, and fluid use cases for consumers. As past regulatory efforts have failed to match the rapid innovation of these apps, the US Food and Drug Administration (FDA) has proposed that the Software Precertification (Pre-Cert) Program and a new risk-based framework could be the solution. OBJECTIVE This study aims to determine whether the risk-based framework proposed by the FDA’s Pre-Cert Program could standardize categorization of top health apps in the United States. METHODS In this quality improvement study during summer 2019, the top 10 apps for 6 disease conditions (addiction, anxiety, depression, diabetes, high blood pressure, and schizophrenia) in Apple iTunes and Android Google Play Store in the United States were classified using the FDA’s risk-based framework. Data on the presence of well-defined app features, user engagement methods, popularity metrics, medical claims, and scientific backing were collected. RESULTS The FDA’s risk-based framework classifies an app’s risk by the disease condition it targets and what information that app provides. Of the 120 apps tested, 95 apps were categorized as targeting a nonserious health condition, whereas only 7 were categorized as targeting a serious condition and 18 were categorized as targeting a critical condition. As the majority of apps targeted a nonserious condition, their risk categorization was largely determined by the information they provided. The apps that were assessed as not requiring FDA review were more likely to be associated with the integration of external devices than those assessed as requiring FDA review (15/58, 26% vs 5/62, 8%; <i>P</i>=.03) and health information collection (24/58, 41% vs 9/62, 15%; <i>P</i>=.008). Apps exempt from the review were less likely to offer health information (25/58, 43% vs 45/62, 72%; <i>P</i>&lt;.001), to connect users with professional care (7/58, 12% vs 14/62, 23%; <i>P</i>=.04), and to include an intervention (8/58, 14% vs 35/62, 55%; <i>P</i>&lt;.001). CONCLUSIONS The FDA’s risk-based framework has the potential to improve the efficiency of the regulatory review process for health apps. However, we were unable to identify a standard measure that differentiated apps requiring regulatory review from those that would not. Apps exempt from the review also carried concerns regarding privacy and data security. Before the framework is used to assess the need for a formal review of digital health tools, further research and regulatory guidance are needed to ensure that the Pre-Cert Program operates in the greatest interest of public health. CLINICALTRIAL

Model-based Cost-effectiveness of State-level Latent Tuberculosis Interventions in California, Florida, New York, and Texas

2020 ◽  
Author(s):  
Youngji Jo ◽  
Sourya Shrestha ◽  
Isabella Gomes ◽  
Suzanne Marks ◽  
Andrew Hill ◽  
...  
Keyword(s):  
New York ◽  
Cost Effectiveness ◽  
Latent Tuberculosis ◽  
State Level ◽  
Cost Effective ◽  
Health Impact ◽  
The United States ◽  
Interferon Γ ◽  
Quality Adjusted Life Year ◽  
System Perspective

Abstract Background Targeted testing and treatment (TTT) for latent tuberculosis (TB) infection (LTBI) is a recommended strategy to accelerate TB reductions and further TB elimination in the United States. Evidence on cost-effectiveness of TTT for key populations can help advance this goal. Methods We used a model of TB transmission to estimate the numbers of individuals who could be tested by interferon-γ release assay and treated for LTBI with 3 months of self-administered rifapentine and isoniazid (3HP) under various TTT scenarios. Specifically, we considered rapidly scaling up TTT among people who are non–US-born, diabetic, living with human immunodeficiency virus (HIV), homeless or incarcerated in California, Florida, New York, and Texas—states where more than half of US TB cases occur. We projected costs (from the healthcare system perspective, in 2018 dollars), 30-year reductions in TB incidence, and incremental cost-effectiveness (cost per quality-adjusted life-year [QALY] gained) for TTT in each modeled population. Results The projected cost-effectiveness of TTT differed substantially by state and population, while the health impact (number of TB cases averted) was consistently greatest among non–US-born individuals. TTT was most cost-effective among persons with HIV (from $2828/QALY gained in Florida to $11 265/QALY gained in New York) and least cost-effective among people with diabetes (from $223 041/QALY gained in California to $817 753/QALY in New York). Conclusions The modeled cost-effectiveness of TTT for LTBI varies across states but was consistently greatest among people with HIV; moderate among people who are non–US-born, incarcerated, or homeless; and least cost-effective among people with diabetes.

scholarly journals Incidence of Benign Meningiomas in the United States: Current and Future Trends

2021 ◽  
Author(s):  
Sonia Bhala ◽  
Douglas R Stewart ◽  
Victoria Kennerley ◽  
Valentina I Petkov ◽  
Philip S Rosenberg ◽  
...  
Keyword(s):  
United States ◽  
Age Groups ◽  
Cancer Registries ◽  
Health Impact ◽  
The United States ◽  
Birth Cohorts ◽  
Benign Meningioma ◽  
Case Count ◽  
Future Burden ◽  
Rate Ratios

Abstract Background Benign meningiomas are the most frequently reported central nervous system tumors in the United States (US), with increasing incidence in past decades. However, the future trajectory of this neoplasm remains unclear. Methods We analyzed benign meningioma incidence of cases identified by any means (eg, radiographically with or without microscopic confirmation) in US Surveillance Epidemiology and End Results (SEER) cancer registries among 35–84-year-olds during 2004–2017 by sex and race/ethnicity using age-period-cohort (APC) models. We employed APC forecasting models to glean insights regarding the etiology, distribution, and anticipated future (2018–2027) public health impact of this neoplasm. Results In all groups, meningioma incidence overall increased through 2010, then stabilized. Temporal declines were statistically significant overall and in most groups. JoinPoint analysis of cohort rate-ratios identified substantial acceleration in White men born after 1963 (from 1.1% to 3.2% per birth year); cohort rate-ratios were stable or increasing in all groups and all birth cohorts. We forecast that meningioma incidence through 2027 will remain stable or decrease among 55–84-year-olds but remain similar to current levels among 35–54-year-olds. Total meningioma burden in 2027 is expected to be approximately 30,470 cases, similar to the expected case count of 27,830 in 2018. Conclusions Between 2004–2017, overall incidence of benign meningioma increased and then stabilized or declined. For 2018–2027, our forecast is incidence will remain generally stable in younger age groups but decrease in older age groups. Nonetheless, the total future burden will remain similar to current levels because the population is aging.

A proposal to leverage high-quality veterinary diagnostic laboratory large data streams for animal health, public health, and One Health

2021 ◽  
pp. 104063872110030
Author(s):  
Craig N. Carter ◽  
Jacqueline L. Smith
Keyword(s):  
Public Health ◽  
Data Streams ◽  
One Health ◽  
Animal Health ◽  
Disease Outbreaks ◽  
Large Data ◽  
Health Impact ◽  
The United States ◽  
Zoonotic Diseases ◽  
Diagnostic Laboratory

Test data generated by ~60 accredited member laboratories of the American Association of Veterinary Laboratory Diagnosticians (AAVLD) is of exceptional quality. These data are captured by 1 of 13 laboratory information management systems (LIMSs) developed specifically for veterinary diagnostic laboratories (VDLs). Beginning ~2000, the National Animal Health Laboratory Network (NAHLN) developed an electronic messaging system for LIMS to automatically send standardized data streams for 14 select agents to a national repository. This messaging enables the U.S. Department of Agriculture to track and respond to high-consequence animal disease outbreaks such as highly pathogenic avian influenza. Because of the lack of standardized data collection in the LIMSs used at VDLs, there is, to date, no means of summarizing VDL large data streams for multi-state and national animal health studies or for providing near-real-time tracking for hundreds of other important animal diseases in the United States that are detected routinely by VDLs. Further, VDLs are the only state and federal resources that can provide early detection and identification of endemic and emerging zoonotic diseases. Zoonotic diseases are estimated to be responsible for 2.5 billion cases of human illness and 2.7 million deaths worldwide every year. The economic and health impact of the SARS-CoV-2 pandemic is self-evident. We review here the history and progress of data management in VDLs and discuss ways of seizing unexplored opportunities to advance data leveraging to better serve animal health, public health, and One Health.

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