Will the Future of Healthcare Shift Towards Value-based Care?

Social-Determinants

Is the future of healthcare shifting towards employing clinical analysis technology to administer value-based care? Analytic technology allows healthcare providers to understand their patient population better using social determinants of health. The analytic technology uses electronic health records to measure health trends amongst the targeted population.

Value-based care is collecting relevant data sources specific to socio-economic and social determinants of health unique to a specific population to plan appropriate interventions. These strategies have the potential to improve health outcomes specifically targeted areas at lower costs. Cost can be lower through a thorough analytical investigation of community needs trends and provider behaviors that increase cost. Carilion Clinic in Virginia is currently utilizing value-based care and analytical technology to serve its community better.

For more information check out this article:

https://www.healthcareitnews.com/news/analytics-social-determinants-drives-millions-value-based-savings-carilion

Image:

https://www.alliancebhc.org/general-news-announcements/alliance-grants-target-social-determinants-health/

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Digital health for the elderly

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Amazon’s new Echo Spot Smart home technology introduces a variety of ways that technology can play a central role in maintaining meaningful interactions between seniors and their caregivers and/or families. The seniors of today possess considerably more technological knowledge, and more comfort with technology, than their predecessors. This helps improve the potential for elders to successfully age at home.

Smart homes appliances provide valuable support to elderly parents, with Skype and FaceTime being good solutions to maintaining healthy interactions across long distances. However, they require both participants in calls to effectively utilize an app. They simply cannot substitute for human interaction.  Amazon’s new Echo Show has the potential to reduce challenges that comes with video calls and new-aged technology. A brilliant idea it may be, there are still issues related to small screen size and placement, but this can spark the candle for more innovative ways to care for the elderly.

 

https://www.theverge.com/2018/1/25/16933440/amazon-echo-spot-ad-family-sales

https://www.foxbusiness.com/personal-finance/heres-the-newest-technologies-for-seniors-and-their-homes

https://www.adweek.com/brand-marketing/amazons-first-ad-for-the-echo-spot-is-about-togetherness-not-shopping/

Screening Social Determinants of Health through EHR Systems

Electronic Health Record (EHR), systems used to collect, analyze, and make referrals, use information provided from patients to provide quality and efficient care provided to tailor the needs of the people. Social determinants of health are social and economic conditions that influence quality of life and health outcomes.

Intertwining electronic health records and social determinants of health provide researchers with better quality data to understand the needs and experiences of the community. Analyzing the social determinants of health gives vision to the social needs of the community to develop community health centers that serve the community at an optimal level.

 

https://www.clinical-innovation.com/topics/ehr-emr/ehr-system-screen-social-determinants-health

Las Cruces-Area Group To Launch Data-Driven Adverse Childhood Experiences Prevention Project​

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This community in New Mexico took the effort to combat childhood trauma to new heights. A group of  Dona Ana county agencies initiated a childhood trauma project using data on Adverse Childhood Experiences (ACEs). A high ACEs score can result in a child’s diminished capacity to learn in school, diminished work performance, or ability to establish healthy relationships. The project was in response to a community movement to end childhood trauma and maltreatment based on a real Protective Services Division of the New Mexico Children, Youth & Families Department Case.

The Adverse Childhood Experiences (ACEs) Prevention Project launched on Aug. 8, with a series of events in Las Cruces.  The program includes a face-to-face classroom experience and five web-based lessons for participants to explore strategies for meeting the needs of families and preventing ACEs and trauma. The group believes through a data-driven citywide process, ACEs can be predictable and preventable.

 

 

http://www.krwg.org/post/las-cruces-area-group-launch-data-driven-adverse-childhood-experiences-prevention-project

Do Socioeconomic Factors Influence Texans’ Decision to Get Vaccinated? – A cartographic Approach

Texas has one of the highest vaccination rates for childhood diseases overall, 97.4%, according to CDC. But the number of children not vaccinated because of their parents’ “personal beliefs”—as opposed to medical reasons—has risen since 2003, when such exemptions were introduced, to more than 44,000 so far in 2017 according to CDC. The 4:3:1:3:3:1:4 series is an overall measure that encompasses many vaccines that are recommended for children. Various demographic factors (sex, gender, race, availability of commercial health insurance) influence the decision to get vaccinated, were looked at.

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The county-level data on the socioeconomic factors were obtained from US Census Bureau (American Factfinder). The health insurance data was obtained from Small Area Health Insurance Estimates (SAHIE). The vaccination rates were obtained from Texas Immunization registry through DSHS. The data was cleaned and geocoded to be analyzed in ArcGIS to produce maps as shown in Figure 1. Pearson’s correlation coefficient was used to analyze the relationship between vaccination rates and independent variable.

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The non-vaccination rates are higher around the major cities of Dallas, Austin-San Antonio, Houston and some northwest Texas counties. Population density has a positive correlation with the non-vaccination rate. Other demographic factors have a positive correlation in certain counties as opposed to others.

 

Source: American FactFinder, Texas Immunisation Registry

The limitation on the immunization data is it being an optional registry so it would not be accurate to run statistics off this information to estimate an immunization rate. In future, it is productive to expand this concept to use regression analysis to try to find the odds of the relationship expressed in the maps and to find if there is a significant association.

Preventive Dental Care for Children in GA – A look at the disparities.

Preventive oral health care is essential for one’s overall health. For children, it is important to address oral health needs earlier in life to prevent oral health issues from forming and progressing into adulthood. Below is a map of the percentage of children in Georgia with financial access to preventive dental care. This map comes from an article written by Cao, Gentili, Griffin, Griffin & Serban (2017) titled, “Disparities in Preventive Dental Care Among Children in Georgia.”

The authors of the article state that financial access is, “the percentage of children who either are eligible for public insurance or have the ability to afford dental care through commercial insurance or ability to pay out-of-pocket,” (Cao et al., 2017). Although there are plenty of children who are eligible to receive public funding for preventive dental care in GA, only 27.9% of the 4,123 dentists in GA who offer preventive dental services to children accept public insurance, (Cao et al., 2017).

What are your thoughts? What does financial access to preventive dental care mean to you? Is this an accurate representation of financial access? I invite you to read more of the article on the CDC’s website, here

GA preventive oral healh care for chilldren

Source: Cao S, Gentili M, Griffin PM, Griffin SO, Serban N. Disparities in Preventive Dental Care Among Children in Georgia. Prev Chronic Dis 2017;14:170176. DOI: http://dx.doi.org/10.5888/pcd14.170176.

Average Fine Particulate Matter (PM 2.5) (µg/m³) By County (2011)

AvgPM_2011

Check out this map that shows the average fine particulate matter (PM 2.5) (µg/m³) by county for the year 2011. From the map we can see clusters with a higher average indicated by the darker shading. For instance, we can see a cluster consisting counties within for Colorado, Nebraska, Kansas and Wyoming. It is also apparent there are higher concentrations in many Southern, Midwestern and Northeastern states compared to western states. States such as, Indiana, Ohio, Tennessee and Kentucky predominantly have a higher average.

By Julia Watson