Category: TECHNOLOGY

Digital-in-Health: Unlocking the Value for Everyone

Digital technology can strengthen health systems, improve health financing and public health, and increase reach to underserved populations, according to a new World Bank report launched today. The report also finds that digital technology and data are especially helpful to prevent and manage chronic diseases, care for both young and aging populations, and prepare for future health emergencies and health risks triggered by climate change.

The report, Digital-in-Health: Unlocking the Value for Everyone, was launched today during the G20 Health Ministers Meeting in Gandhinagar, India. It presents a new way of thinking from simple digitization of health data to fully integrating digital technology in health systems: Digital-in-health. This means, for example, infusing digital technologies in health financing, service delivery, diagnostics, medical education, pandemic preparedness, climate and health efforts, nutrition, and aging.

The report also underscores that the successful use of digital technologies must be inclusive of all population groups, and ensure access to digital infrastructure, modern technologies, and skills, especially for vulnerable people.

“Designed with people at the center, digital technology can make health services more personal, prevent healthcare costs from increasing, reduce differences in care, and make the job easier for those who provide health services,” said Mamta Murthi, Vice President for Human Development, World Bank. “We hope that this report will give governments confidence and practical guidance, regardless of the country’s stage of digital maturity or fiscal challenges.”

Improving health is getting harder, not easier. Health systems face serious and growing challenges and policy decisions are too often not based on reliable data.  It is estimated that some countries use less than 5% of health data to improve health which means that decisions are not based on data or data is not used effectively to make improvements. Within challenging fiscal environments, people-centered and evidence-based digital investments can help governments save up to 15% of health costs. The report presents pragmatic, low-cost actions to improve digital-in-health, no matter the maturity of a country’s systems or digital infrastructure. For example, better health data governance and standards to ensure systems can readily connect and exchange information are not costly but will be game changing in reducing siloed digital solutions and fragmentation.

“In India, we have shown that digital innovations such as tele-consultations have reached more than 140 million people and provided accessible, affordable and efficient healthcare for everyone,” said Mansukh L Mandaviya, Minister for Health and Family Welfare, India. “We believe a digital-in-health approach can unlock the value of digital technologies and data and has the potential to prevent disease and lower healthcare costs while helping patients monitor and manage chronic conditions.” 

 

To help countries embrace a digital-in-health approach, the report proposes three essential areas to guide investments:

  1. Prioritizeevidence-based digital investments that tackle the biggest problems and focus on the needs of patients and providers.
  2. Connect the regulatory, governance, information, and infrastructure dots so that patients know that data is safe and health workers can use digital solutions transparently.
  3. Scale digital health for the long run based on trust with sustainable financing, and improved capacity and skills for digital solutions.

It will take global, regional, and country leadership to make digital-in-health a reality. The report recommends strong country leadership involving all relevant sectors and stakeholders, including civil society. Digital technology and data improvements will involve investments beyond the health sector and new partnerships with the private sector. A digital-in-health mindset needs to be a routine aspect of annual health system planning, budgeting, and implementation.

The World Bank is committed to helping low-and middle-income countries to make digital-in-health a reality to improve health for everyone. Over the past decade, the World Bank has invested almost $4 billion in digital health including in health information systems, digital governance, identification systems, and infrastructure.

For more information, including a copy of the new report, Digital-in-Health: Unlocking the Value for Everyone, please visit:

Website: www.worldbank.org/en/topic/health

Twitter: http://www.twitter.com/WBG_Health

Facebook: http://www.facebook.com/worldbank

YouTube: http://www.youtube.com/worldbank

 

Conceptualizing the Mechanisms of Social Determinants of Health: A Heuristic Framework to Inform Future Directions for Mitigation

A large body of scientific work examines the mechanisms through which social determinants of health (SDOH) shape health inequities. However, the nuances described in the literature are infrequently reflected in the applied frameworks that inform health policy and programming.

We synthesize extant SDOH research into a heuristic framework that provides policymakers, practitioners, and researchers with a customizable template for conceptualizing and operationalizing key mechanisms that represent intervention opportunities for mitigating the impact of harmful SDOH.

In light of scarce existing SDOH mitigation strategies, the framework addresses an important research-to-practice translation gap and missed opportunity for advancing health equity.

Conceptualizing the Mechanisms of Social Determinants of Health!

I. SDOH
Health inequities are most often understood as associated with the social determinants of health (SDOH)

II. Opportunity
A practical, heuristic framework for policymakers, practitioners, and researchers is needed to serves as a roadmap for conceptualizing and targeting the key mechanisms of SDOH influence

  • Unifying principles

1. SDOH are underlying causes of health inequities
-> Meaningful community engagement in data generation and interpretation for understanding and mitigating underlying health inequity drivers and multilevel resilience factors

2. SDOH shape health inequities through contextual influences
-> Development, evaluation, and scale up of multilevel interventions that address the mechanisms of SDOH at the structural, psychosocial, and clinical/biomedical levels

3. SDOH contextual disadvantage is not deterministic
-> Adoption of individualized/differentiated, decentralized, and community-based service delivery models

4. SDOH shape health over the life course
-> Proactive intervention focused on prevention and health promotion as well as restorative care to maintain and improve physical, mental, and psychosocial functioning and quality of life

5. SDOH operate through biological embedding
-> Greater prioritization of harmful SDOH mechanisms and mitigation of their biological impact in clinical education and practice, including investment in biomarkers for early detection of and intervention on emerging disease trajectories

6.SDOH operate intergenerationally
-> Prioritization of family-based approaches to restorative health care, prevention, and health promotion

7. SDOH shape clustering and synergies of health inequities
-> Greater integration of comprehensive, interdisciplinary, team-based health services delivered within a value-based framework and at the top of providers’ licenses

8. SDOH mechanisms to produce health inequities
-> Departure from vulnerability- and deficiency-focused paradigms for understanding health inequities toward multilevel resilience-focused paradigms for reducing health inequitiess

An Organizing Framework of SDOH Mechanisms

1. Underlying causal factors
-> Two distinct classes of social influence: SDOH capital and SDOH processes

2. Mediating factors
-> Two mechanisms: environmental and behavioral exposure and biological susceptibility

3. Moderating factors
-> Resilience – as collective action that supports the ability of communities to thrive when confronted with structural challenge

4. Health inequity outcomes
-> The impact of SDOH mechanisms on health inequities is dependent on the broader patterns of morbidity within the community of interest

Check out the article by Marco Thimm-Kaiser, Adam Benzekri and Vincent Guilamo-Ramos here:

https://lnkd.in/e57GXthQ

How A.I. Could Help Medical Professionals Spend Less Time on Admin Work and More Time on Care

Some entrepreneurs are betting that generative A.I. tech like ChatGPT can provide a solution to the medical industry’s burnout crisis.

A survey of 1,000 Americans and 500 health care professionals conducted by Tebra–an all-in-one digital platform used by medical providers to manage their practices–showed that one in 10 providers is currently using A.I., while 50 percent of surveyed respondents signaled an intention to adopt the tech in the future, particularly in use cases involving data entry, appointment scheduling, and medical research.

Luke Kervin, Tebra’s founder, says that if A.I. can help providers to stave off burnout by increasing efficiency, saving costs, and allowing them to spend less time on admin work and more time helping people, it will likely see mass adoption by the industry. “When we talk to our providers about what keeps them up at night, it’s always burnout,” adds Kervin, “and a lot of that burnout comes from having so much admin work to do.”

Ironically, the advent of electronic medical records (EMRs) was meant to help physicians save time that had previously been spent maintaining analog health charts, but some practitioners are now spending an increasing amount of time behind the computer. Indeed, a 2017 study published in the Annals of Family Medicine found that in an 11.4-hour workday, physicians spent an average of nearly six hours on tasks related to administrative tasks, like data entry and inbox management, which contributed to their burnout.
Some solutions are already available, such as from Microsoft-owned A.I. business solutions provider Nuance. According to a case study, physicians at the Nebraska Medicine health system were frustrated with the time and effort required to complete patient notes, so Nuance provided an A.I.-powered voice recognition solution, allowing providers to fill out notes using just their voice. The change was a success, with 94.2 percent of surveyed physicians saying that the tech helped them to save time and do their job better.

Another company working on A.I.-powered solutions for both providers and patients is New York-based mental health employee benefits company Spring Health, which has raised nearly $400 million and attained a $2.5 billion valuation since its 2016 founding. Once a client has signed up for the service, they fill out a short assessment containing a series of questions about both their medical history and the current state of their mental health. The company’s machine-learning algorithm then crafts a personalized care plan that includes both wellness recommendations like daily routines, and specific recommendations for nearby mental health care providers.

Spring Health co-founder Adam Chekroud says that they’ve barely begun to scratch the surface of how automation could improve business for health care providers, adding that the company recently rolled out a new functionality that enables providers to “translate” their shorthand notes from patient meetings into full sentences with the use of a large language learning model.

Chekroud is also excited about the possibility of integrating chatbots as a way of helping people find providers who are a perfect fit for them, and described one prototype in development. “Our chatbot could ask, ‘Is there anything you want us to know that would help us find you a provider?’” According to Chekroud, the patient could answer with something like, “I’m very religious and I want a provider who could do faith-based treatment” or “I’m going through some gender identity issues and I want to have a provider that understands that.” The chatbot would then scan through the Spring Health network to surface providers with those desired traits.

A small number of providers are even beginning to use A.I. to help them make diagnoses by using tools such as Med-PaLM, Google’s large language model for medical information. But when it comes to using chatbots as virtual therapists, Chekroud is much less convinced. He concedes that generative A.I. is surprisingly capable of imitating empathy, “but we still have this fundamental problem that you’re talking to a robot. A robot can’t know what you’re going through. Nothing can replace that human connection.”

Πηγή: inc.com