Socio-technology Plan for Smart-home Healthcare Integration

CS875 – Individual Project 5

John Brower

Colorado Technical University

 

Individual Project 4

Socio-technology Plan Introduction

            In 1950 Isaac Asimov published the Three Laws of Robotic in I, Robot (Rotenberg, 2021). Asimov correctly identified the challenges associated with technology integration with humanities culture. The adoption of innovative technology requires careful planning when integrating machines into the human experience. The socio-technology plan facilitates the blending of devices with the intent of improving lives. While Asimov's laws are insightful, a more helpful guide to technology integration is the ethics associated with the safety and security of the adopted technologies.

            As the population ages, the current expense of healthcare is unsustainable and requires innovative solutions to ensure the quality of life and efficient delivery of care (Hosseini, 2015). The reality healthcare faces are the need to care for chronic conditions associated with advanced age. Management of chronic health conditions occurs outside of the hospital, but many individuals experience a health crisis due to the lack of observation and timely intervention. Adopting innovative technologies within the home can prevent hospitalization and mortality through early detection and intervention (Thompson et al., 2020).

Scope

            There are three primary areas of focus needed for a smart home: monitoring devices, communications, and data analysis. Many technologies are available on the market that gathers biometric data, such as wearable smart devices, smart scales, and various diabetic monitoring aids. The sensitivity of the smart devices improved over time and now provides highly reliable information (Bell, 2019).

            The second area of concern is the transmission of information. The Internet of Things is a highly adaptable network integrating devices with services all over the globe. Simply stated, the vastness of the web defies national borders and makes possible the exchange of large quantities of data (Marakhimov & Joo, 2017). The Fast Healthcare Interoperability Resources or FHIR standard provides a mechanism to securely transmit information through the web (Hong et al., 2020). Image 1 outlines the integration of in-home monitoring using FHIR and healthcare services

Image 1. In-home health integration monitoring technology

            The final objective is information analysis and timely response to prevent a health crisis. Biometric data is highly dimensional that complicates the study of information. Machine learning algorithms offer the most suitable mechanism to evaluate the generated data.

            The legal questions regarding the protection of healthcare providers are the most significant obstacle in adopting innovative home technologies (Kun-Hsing et al., 2018). The current market places the burden of gathering and analyzing data on the device owner. However, there are few mechanisms in place to evaluate data automatically and provide feedback. I believe a logical place to evaluate data and provide intervention is with the patient's provider. However, very few healthcare providers will accept that responsibility without legal protections and appropriate funding.

Purpose

            In the United States, the inpatient occupancy rate averages 65.9% of the available beds (Ravaghi et al., 2020). Yet, despite the generous hospital capacity, the cost of care increases annually (Crowley et al., 2020). As a result, the federal government and health care organizations struggle to find ways of containing costs. A way to achieve that objective is to prevent expensive inpatient services with electronic monitoring and timely intervention.

Supporting Forces

            The primary supporting factor is financial. Image 2 uses data published by Macroeconomic Advisor and the relationship between healthcare costs compared to the rest of the American economy. The increasing cost of providing services is growing beyond our economy's capacity to pay for care. Many reasons exist that contribute to cost escalation, including the ever-increasing medication costs (Currin, 2020) and expensive in-hospital diagnostics and medical staff care.

Image 2. Comparison between Healthcare costs and the GDP

            The technological advances in computing science and the decreasing cost of electronics open the door to in-home monitoring. In addition, Healthcare professional's and patient's trust in the technology has increased over time as device reliability and accuracy improved (Chandra & Skinner, 2011). While the adoption of in-home monitoring does not directly reduce pharmaceutical costs, the early detection of illness may enhance the effectiveness of less costly medications and avoid the need for expensive hospital services.

Challenging Forces

            The political realities of healthcare are very complex. On the one hand, you have the single payor crowd, and the other side is the free-market economists. The tug of war between the two opposing forces is about the control of costs. The Centers for Medicare and Medicaid Services or CMS manages the largest payor of healthcare in the nation and exerts enormous influence over care providers. Through this influence, CMS promotes practices and behaviors, including adopting technologies such as FHIR that contribute to cost reductions ("HHS Finalizes Historic Rules to Provide Patients More Control of Their Health Data," 2020). However, CMS and the political institution show little interesting in funding the movement of services from hospitals to homes (Cai et al., 2020).

            Another challenge is the healthcare provider's acceptance of responsibility to act on the biometric information. Many providers cite malpractice lawsuits as a reason to reject the adoption of in-home monitoring (Sharma et al., 2019). Finally, customers expect information sent to the provider to be acted upon by the provider, and the provider expects compensation in return for those services.

Methods

            The best method to address the complexities of in-home monitoring is the divide and conquer approach. The structured design process breaks a big problem into simpler challenges that have straightforward solutions. The discipline of Systems Engineering requires systems to have a functional purpose and operational objectives. Adopting a structured design process provides a model for the system engineer to identify the separate systems and resources needed to achieve the purpose and objectives (Blanchard & Fabrycky, 2011).

 

Individual Project 5

Models

            The use of socio-technology systems in healthcare informatics typically involves integrating hardware and software, clinical content, human-computer interface, workflow and communication, organizational business rules, regulatory concerns, and monitoring and measurement (Sittig & Singh, 2010). Historically, there are several models used in the development of healthcare technology integration: Roger's diffusion of innovations theory (Sahin, 2006), Venkatesh's unified theory of acceptance and use of technology (Baishya & Samalia, 2020), Hutchin's theory of distributed cognition (Hollan et al., 2000), and Norman's 7 step human computers interaction model (Dubberly et al., 2009). Image 1 outlines the systems integration models, and image 3 outlines the socio-technology model based on Norman's 7 step model.

Image 3. Adapted from Norman's 7-step socio-technology integration model

 

Analytical Plan

            The integration incorporates elements that commercially exist, such as wearable smart devices and electronic medical records, with the ultimate goal of decreasing inpatient hospitalization and reduction in medical costs by detecting health deterioration before patient observed symptoms. The analysis does not focus on monetary objectives despite that as a stated goal. Instead, the analytics focuses on the system's performance and the detection and follow-up of clinically significant changes in biometric measurements. The below list identifies four measurable goals that determine success.

·         Measure the number of inpatient days before and after implementation for participating audience

·         Determine algorithm efficiencies in the identification of health changes by identifying true positives vs. false negatives

·         Determine algorithm efficiency in the analysis of high dimensional data

·         Identity provider follow-ups for algorithm-generated positive results (both true and false)

Anticipated Results

            In 2013 Colin Powell argued in an opinion article that healthcare needed to change how the community delivers unscheduled care. The specific argument challenges the number of provider referrals to emergency rooms and identifies the 28% increase in emergency room visits. He further argues many of those visits are unnecessary due to the non-critical discharge diagnosis (Powell, 2013). Recently, United Health Care announced a plan to deny claims for emergency room visits they deem unnecessary (Freeman, 2021). Both of these points address the industry's concern with escalating costs. In addition, implementing this technology may identify changes in health before costly hospital visits and proactively direct the patient to a lesser level of care.      

Conclusion

            Computing technology inundates many segments of the American community. While some may argue unequal distribution between communities based on wealth and location, most communities have access to the technology, if not the means to pay for it. Image 4 shows the distribution of technologies is prevalent yet unevenly divided in many American households (Andersn & Kumar, 2021).

Image 4. Distribution of technology in American households by income

            Despite access to technology, the instruments driving this socio-technology integration are widely available across the country. This technology's availability increases the likelihood of adopting an in-home patient monitoring solution. I would argue that Medicare and Medicaid spending on these solutions is cheaper than in-hospital treatment.

            There remain several significant hurdles that need conquering before adopting this technology integration with the human experience. Perhaps the most notable is the trust people have with the technology. Deploying the solution in a person's home involves a level of privacy invasion and subsequent sharing of information with another community. There are legitimate reason's to distrust an organization's motives behind data gathering (Rivero, 2020).

FHIR protects data in motion from bad actors but does not protect against exploitation by businesses. The pollical systems need to address the populaces concerns about data protection and exploitation of personal information (Evans, 2016). The Health Insurance Portability and Accountability Act defines the standards business must take to protect patient privacy. Still, this legislation does not address the responsibilities individuals and organizations must take on that information (Annas, 2003). Any future healthcare technology integration with the human experience must address the legal obligations and healthcare provider's protections from litigation (Ramanujan & Kesh, 2020).

The reduction of healthcare costs needs innovative solutions, and in-home monitoring and electronic medical record integration offer one avenue to achieving those savings. The integration of technology into our communities remains a challenge, with many questions currently without answers.

Areas of Future Research

There are many unanswered questions regarding the adoption of privacy-invasive technologies into the home environment. Community leaders may question the value in-home monitoring provides and its effectiveness in reducing costs. Proving the value and savings is challenging. Additional research should explore the relationship between technology adoption with the use of pilot studies in at-risk populations. Evaluating the technology benefit to the most vulnerable people at risk for acute deterioration in health is essential in determining inpatient admission reductions.

Further research into efficient machine learning algorithms analysis of high dimensional data is essential to adopting in-home monitoring. In addition, the collection of biometric data, such as vital signs and blood glucose monitoring, results in the formation of large datasets. The analysis of this information is impractical for a provider to review and requires electronic tools. Two promising technique involves dimensional data reduction using vector quantization and multidimensional scaling (Fodor, 2002).

 

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