The Far-Reaching Telehealth and Telemedicine on Services and Technologies

#### **Chapter 7**

## Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017 and Present

*Xiaoni Zhang, Haoqiang Jiang and Gary Ozanich*

#### **Abstract**

The clinical decision support systems (CDSs) for diabetes have improved significantly over the years. Multiple factors serve as driving forces for the uptake of CDSs. Newer technologies, initiatives, government mandates, and a competitive environment collectively facilitate advancement in diabetes care. This book chapter summarizes global CDSs development in recent years. Our review of the past few years' publications on CDSs for diabetes shows that the United States is leading the world in technology development and clinical evidence generation. Developing countries worldwide are catching up in CDSs development and standards of patient care. Though most CDSs and published studies are on diabetes diagnosis, treatment, and management, a small portion of the research is devoted to prediabetes and type I diabetes. Increased efforts worldwide have been devoted to artificial intelligence and machine learning in diabetes care.

**Keywords:** clinical decision support systems, diabetes care, machine learning, artificial intelligence, A1C, patient engagement, outcomes, clinical inertia

#### **1. Introduction**

Globally, chronic care conditions burden society with high costs and diminished quality of life for affected individuals. According to the Center for Disease Control and Prevention (CDC), more than one in 10 Americans ha diabetes mellitus, commonly referred to as type-2 diabetes (T2DM), and approximately one in three has prediabetes. Diabetes was the seventh leading cause of death in the United States in 2017. People with diagnosed diabetes, on average, have medical expenditures 2.3 times higher than those without diabetes [1], and 25% of all medical costs in the United States are spent on caring for people with diabetes. Diabetes can result in disabling complications, comorbidities, and reduced life expectancy. Effective management of diabetes is important to improve the quality of life for diabetics as well as improve population health and control medical costs. Attention and interventions are needed to address the issue of rising costs. Clinical decision support systems (CDSs) may

offer the solution to rising costs, quality of care, patient engagement, patient-centered care, personalized medicine, clinical inertia, and clinical outcomes.

According to KBVResearch, the global CDSs market will grow from 2.9 billion in 2017 to 8.9 billion in 2027 [2]. The adoption of Electronic Health Record (EHR) and CDSs has been on the rise across the globe. Developed countries lead the development and implementation of CDSs. Several factors contribute to the increased acceptance and adoption of CDSs: general acceptance of using technologies across the entire healthcare spectrum, including adherence to clinical guidelines, evidence of improved clinic outcomes, government incentives, compliance/regulatory requirements, and operational efficiency. In this book chapter, we provide an overview of recent evidence on CDSs for diabetes care by searching relevant publications in CINAL, PsychInfo, Web of Science, Scopus, Medline, and PubMed from 2017 to the present.

#### **2. Clinical decision support systems for diabetes care**

#### **2.1 Diabetes care**

Diabetes is a chronic disease. Adequate diabetes care requires attention to biomarkers such as blood pressure, cholesterol, blood sugar level, and lifestyle changes. Care typically involves management of blood pressure, lipids, smoking, glucose, weight, screening for eye, foot, renal and vascular complications, and immunizations. It is common that patients with diabetes also have one or more other comorbid conditions. Thus, caring for diabetics is a team effort, and many providers may be involved, including various types of physicians or nurse practitioners, pharmacists, case managers, dieticians, and specialty doctors such as cardiologists, dentists, ophthalmologists, others. The literature has consistently reported a gap between current diabetes care practice and recommended diabetes care standards. This includes the concept of clinical inertia or the failure to start or accelerate a current or new therapy when appropriate. Clinical inertia may be due to the clinician's lack of knowledge or inexperience with new therapeutic interventions and drugs available to treat diabetes [3].

Many IT-based interventions have been developed to improve adherence to the quality of care standards for chronic illnesses such as diabetes. CDSs for diabetes have been developed to address prediabetes screening, type I, type II, and gestational diabetes diagnosis, treatment, and care. **Figure 1** shows the publications related to CDSs in diabetes. Though CDSs predated EHR, it is well documented in the literature that the adoption of CDSs is low [4].

#### **2.2 Clinical decision support (CDS)**

A clinical decision support (CDS) is a computerized system that uses case-based reasoning to assist clinicians in various decision-making such as assessing disease status, diagnosis, selecting appropriate therapy, or making other clinical decisions [5]. CDSs are typically used at the point of care where clinicians can make treatment decisions either based on their own knowledge or by combining their knowledge with patient characteristics or recommendations provided by the CDS through a clinical disease-specific knowledge base. CDSs provide alerts, reminders, or feedback to a care team [6]. A CDS can improve healthcare delivery by improving medical decisions with targeted clinical knowledge, patient information, and other health information [7].

*Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017… DOI: http://dx.doi.org/10.5772/intechopen.108509*

**Figure 1.** *CDSs research areas.*

#### *2.2.1 History of clinical decision support*

The idea was generated in the 1950s. In the late 1960s, F. T. deDombal and his associates at the University of Leeds studied the diagnostic process. They developed the Leeds abdominal pain system, a computer-based decision aid using Bayesian probability theory to explain seven possible causes of acute abdominal pain. In the 1970s, Stanford University developed MYCIN, rule-based decision support using a reasonably simple inference engine and a knowledge base of 600 rules. Later, Help was developed, and both MYCIN and HELP could generate alerts when abnormal factors were observed. Earlier studies on CDSs report that the use of automated clinical guidelines for diabetes in general practice did not result in a clinically significant change in doctors' behavior or in patient outcomes [8].

#### *2.2.2 Components of CDSs*

**Figure 2** depicts the components of a CDS. Typically, a CDS consists of a knowledge base, inference engine, and communication mechanism. The knowledge base contains facts, best practices, clinical guidelines or protocols, drug interactions, drug allergies, and logical rules. The inference engine combines patient-specific data

(demographic data, medical history, family history) with clinical knowledge and performs reasoning. The communication mechanism takes patient data as input and produces output including alerts, reminders, summaries, etc.

Different technologies are used to build CDSs. Some use open-source software. For example, Protégé and WebProtégé are free software programs for building ontology knowledge solutions, and Jena is the Java rule-based inference engine. WebProtégé builds drug knowledge, and Jena evaluates the antidiabetic medications reasoning module [9].

#### **2.3 Benefits of clinical decision support systems in diabetes**

Digital transformation involves fundamentally rethinking healthcare delivery processes, treatments, and services from a technology-enabled perspective. CDSs promote diabetes care by facilitating evidence-informed insulin use, improving blood glucose control, and quality indicators in caring for patients with diabetes. Given the complex undertaking for clinicians, CDSs may simplify and improve the care process and patient outcomes. CDSs could be valuable when delivering medical care to better match patients' preferences and biological characteristics. Normally, CDSs automatically provide specific treatment recommendations.

Commercial developers typically promote CDSs to improve clinical decisionmaking, reduce medication errors and misdiagnoses, provide consistent and reliable information, enhance operational efficiency, increase patient satisfaction, improve quality of care, and lower costs. The literature echoes some of the claims made by these vendors. For example, a systematic review suggests that CDSs reduce unwarranted practice variation, improve healthcare quality, reduce waste in the healthcare system, and decrease the risk of overload and burnout among clinicians [10]. Some devoted efforts to developing a user-friendly, comprehensive, fully integrated web and mobile-based clinical decision support and monitoring system for the screening, diagnosis, treatment, and monitoring of diabetes [11].

*Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017… DOI: http://dx.doi.org/10.5772/intechopen.108509*

#### *2.3.1 Outcomes*

Recent studies show positive outcomes in controlling glucose levels for patients with diabetes. A CDS was associated with improving the comprehensive control of blood pressure, LDLc, and HbA1c for diabetics in primary care [12]. Glucose Path, an AI-enabled CDSs for diabetes, effectively reduces the glucose level of patients with poorly controlled diabetes in the Medicaid population. These CDSs facilitate team-based care allowing a cost-effective solution to be produced for patients [13]. GlycASSIST, another diabetes CDS, facilitated treatment intensification and was acceptable to patients with diabetes and general practitioners [14]. A CDS tool on the management of diabetes in small- to medium-sized primary care practices participating in Delaware's patient-centered medical home project finds the use of CDS is correlated with greater reductions from baseline in hemoglobin A1c and low-density lipoprotein cholesterol, and more patients achieving treatment goals, aiding physicians and staff in better clinical decision-making [15]. EHR CDS was successful in reducing hyperglycemic events among hospitalized patients with dysglycemia and diabetes and inappropriate insulin use in patients with type 1 diabetes [16].

#### *2.3.2 Clinician satisfaction*

Additional studies have found clinician satisfaction with CDSs use in treating diabetes and facilitating treatment intensification by the general practitioners [14]. In a cluster-randomized trial, an EHR-linked, web-based CDSs significantly improved glucose and blood pressure control in diabetes patients. The CDS has high use rate and clinician satisfaction. As a result, users are willing to recommend the CDS to others [17]. Furthermore, recent evidence shows that the majority of physicians are satisfied with CDSs [18]. The CDS was feasible and acceptable to GPs [19].

#### *2.3.3 Operational efficiency*

CDS for diabetes can help with disease management, and its web-based system CDS provides on-time registration, reports of diabetic prevalence, uncontrolled diabetes, and diabetic complications and reduces the rate of mismanagement of diabetes [20]. In a qualitative evaluation of a standalone CDSs for medication reminders, CDSs were found to improve adherence to evidence-based guidelines and support a more efficient ordering process for providers; providers are satisfied with the CDS for diabetes [21]. CDSs improve healthcare professionals' adherence to suggested insulin doses and workflow tasks. The decision support system facilitates safe and efficacious inpatient diabetes care by standardizing treatment workflow and providing decision support for basal-bolus insulin dosing [22]. The CDSs integrated with the Epic EHR at the University of Utah enable clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. The proposed analytical method outperformed previous machinelearning algorithms on prediction accuracy [23].

#### **2.4 Barriers**

Despite the benefits documented in the literature, there are barriers to using CDSs. Prior studies suggest time and reimbursement [15], interference with established workflow, unhelpful or irrelevant recommendations, and time pressures [24]. In

practice, time constraints, patient overpopulation, and complex guidelines require alternative solutions for real-time patient monitoring. Physician guidelines use rates for diagnosis, treatment, and monitoring of diabetes are very low. To successfully implement a CDS, organizations must conduct adequate validation of programs, evidence and knowledge-based assimilation, users' feedback, widespread implementation in collaboration with stakeholders, and consistent evaluation of programs' impact [16]. In order for the CDSs to be effective, the CDS should be conceived as part of a broader, coherent, and department-wide quality improvement strategy, where a clinical quality gap between current patient outcomes or processes and the desired end state has been clearly identified and carefully measured.

#### **3. Global overview of CDSs**

This section covers the global development of CDSs; two subsections are created to highlight leading CDSs in industrialized countries and developing countries on technological infrastructure, practice habits, and patient expectations.

#### **3.1 Industrialized countries**

In Europe, C3-Cloud is a European Union's initiative to implement digital health Europe; it is a multinational effort for integrated patient-centered care in the cooccurrence of chronic diseases. C3-Cloud has a group of 12 partners across seven countries in Europe. The care for patients with multiple chronic conditions is complex; it is common that patient data are located across multiple systems and in silos; it is difficult to get a complete, accurate, and reliable view of patients' medical history. C3-Cloud project aims to build an integrated care platform, so clinicians have better and complete patient information to make clinical decisions; such systems address the increasing demand for improved health outcomes of patients with multiple chronic conditions.

In addition to C3-Cloud addressing multiple chronic conditions, the MOSAIC project in European Union particularly focuses on decision support for diabetes; this project takes a participatory development approach; it applies persuasive design techniques and business modeling to define three phases: (1) user needs, (2) system implementation, and (3) evaluation of the use of CDSs in diabetes management. Qualitative studies using focus groups were used to compile system requirements to gain new insights in the definition of effective Decision Support Systems to deal with the complexity of diabetes care [25].

Several countries (Turkey, Spain, the United Kingdom, Sweden, Finland, and France) collaborated and developed an ICT infrastructure with guidelines to enable personalized care plan management for addressing the needs of patients with multi-morbidity. The team designed 43 logical flowcharts of four disease guidelines (Type 2 Diabetes, Heart Failure, Renal Failure, and Depression) and implemented 181 CDS rules [26].

In Italy, a multidisciplinary research team consisting of doctors, clinicians, and IT engineers develop a fuzzy inference machine to improve the quality of the day-to-day clinical care of type-2 diabetic patients at the Anti-Diabetes Center. This CDS has the function of remote patient monitoring, which includes the ability to monitor a patient regularly from home. This may help to reduce hospitalizations or other acute events [27].

#### *Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017… DOI: http://dx.doi.org/10.5772/intechopen.108509*

In Belgium, a cluster-randomized trial with before-and-after measurements of a CDS was conducted in Belgian Primary Care Practices over 1 year between May 2017 and May 2018. The majority of physicians were satisfied with the EBMeDS system. Clinicians report many benefits of using CDS, including rapid access to (patient-specific) drug interactions, problems, evidence-based links, etc. Clinicians do not need to perform extensive searching for guidelines. On the disadvantage side, clinicians mention the time required to use the system, the increased alertness by the system, and incorrect reminders. The clinical trial concluded that EBMeDS did not improve diabetes care in Belgian primary care despite the benefits. However, this trial has a significant drop-out rate of 43%. This high drop rate may weaken the conclusion drawn from this study. Further analysis shows the lack of improvement was mainly caused by inadequate software training, EHR data transfer issues, auto coding of lab results, and technical and reporting issues [18].

Another study on CDS for diabetes in Belgium tackles the inappropriate tests as they are a waste of healthcare resources with a pragmatic, cluster-randomized, openlabel, controlled clinical trial. This CDS is integrated into a computerized physician order entry (CPOE) to examine the appropriateness and volume of laboratory test ordering and diagnostic errors in primary care. The results show that a CDS within the CPOE improves the appropriateness of lab tests and decreases the volume of laboratory test ordering without increasing diagnostic error [28].

In Saudi Arabia, an evaluation study of EHR integrated CDS reports no significant improvement in chronic disease outcomes [29]. In South Korea, a CDS for Diabetes was developed based on the innovative integration of ontology and fuzzy-ruled reasoning with real data sets. This CDS has an open architecture that is scalable, extensible and increases accuracy in diagnosing diabetes [30].

In Taiwan, a CDS with a focus on antidiabetic medication recommendations was developed based on the guidelines of the American Diabetes Association and the European Association for the study of diabetes. The CDS enables doctors' clinical diagnosis and decision-making for specialty physicians, nonspecialty doctors, and young doctors with their drug prescriptions. The physician evaluation of the system shows that 87% think the system is useful, and 85% are satisfied with the CDS in their care of diabetes patient [9].

In Australia, a prototype (GlycASSIST) is integrated into an electronic medical record containing evidence-based guidelines. GlycASSIST helps general practice and patients during encounters for setting glycated hemoglobin (HbA1c) targets and intensifying treatment. Interviews and focus groups are conducted with clinicians, including four General Practices, five endocrinologists, three diabetes educators, and six patients with type 2 diabetes. Clinicians and people with diabetes believe that GlycASSIST is useful in individualized treatment intensification. They recommended that GlycASSIST enhances the visual appeal and allows clinicians to overwrite recommendations. In addition, clinicians requested CDS be easily navigated and have greater prescribing guidance [14].

In Turkey, a web and mobile-based application will be developed, which allows the physician to remotely monitor patient data through mobile applications in real time. This system will perform the function of screening, diagnosis, treatment, and monitoring of diabetes diseases. The developed CDS will be tested in two stages: first, the usability, understandability, and adequacy of the application will be determined. Second, a parallel, single-blind, randomized controlled trial will be implemented. Diabetes-diagnosed patients will be recruited for the CDS trial by their primary care physicians [11]. GlycASSIST was able to achieve its purpose of

facilitating treatment intensification and was acceptable to people with T2D and GPs. The GlycASSIST prototype is being refined based on these findings to prepare for quantitative evaluation [14].

In Canada, a CDS assists primary care practitioners in applying standardized behavior change strategies and clinical practice guidelines-based recommendations to an individual patient and empowers the patient with the skills and knowledge required to self-manage their diabetes through planned, personalized, and pervasive behavior change strategies. A qualitative study was then conducted to evaluate usability, functionality, usefulness, and acceptance [31].

In summary, CDSs developed in industrialized countries typically incorporate evidence-based practice into the design and development. The commonly followed guidelines are either published by American Diabetes Association or the European Association. Recent findings report a more positive user experience with CDSs, user acceptance, operational efficiency, and clinical outcomes.

#### **3.2 CDSs in developing countries**

Developing countries face far greater challenges and barriers than industrialized countries managing chronic diseases. Economic backgrounds, lack of resources, and the absence of some laboratory tests may make clinical guidelines published by international associations not applicable to developing countries. In Sri Lanka, about 11% of its total population has diabetes [32]. A CDS for diabetes was developed through two stages: first, mapping the diabetes-related clinical guidelines using the business process model and notation 2.0 for type 1 and type 2 diabetes and gestational diabetes; second, treatment plans were developed with guidelines using flowcharting. Domain experts were consulted to design and evaluate the ontology. Several real-life diabetic scenarios are used to validate and evaluate the ontology [33].

In Egypt, data mining techniques were used to develop classifiers for the early diagnosis of diabetes. An ensemble algorithm significantly outperforms all other classifiers. Such an effort is essential in building a personalized decision support system, aiding physicians in their daily clinical practice [34].

In Iran, a web-based CDS for diabetes diagnosis and management was developed using ASP.Net MVC server technology, Razor engine, SQL Server database, HTML 5, CSS 3 world standard, and Ajax technology. The diabetes CDS is built following the American Diabetes Association and American Association of Clinical Endocrinologists (AACE) guidelines and physical activity 2017 guidelines recommended by the Netherland. Its interface is user-friendliness and easy to use. The interface displays demographic data, past medical history, laboratory tests, lifestyle, and family history. The web-based system allows for on-time registration, better reporting on diabetic status (uncontrolled diabetes, diabetic complications), and reducing the rate of mismanagement of diabetes. It helps the physicians in managing the patients more effectively [20].

In India, the cost of early diagnosis of diabetes is a barrier for many people to get the laboratory testing done. Various machine learning algorithms are integrated with a CDS to assess diabetes [35].

In summary, developing countries have improved their technological development in patient care. However, evidence-based guidelines are not consistently incorporated into the design of CDSs. Interestingly, developing countries explore data mining and machine learning in an innovative way. Algorithms and predictive models are developed to predict prediabetes and diabetes without any lab tests.

*Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017… DOI: http://dx.doi.org/10.5772/intechopen.108509*

Predictive models could not be 100% accurate. Clinicians and data scientists need to work together to determine the acceptable level for model performance. There will be some false positive or false negative. Data scientists need to work with clinicians to determine the pros and cons of false positive and false negative. In the area of prediabetes, false positive may not produce detrimental effect than false positive. Then the models that produce false positive may be more acceptable than false.

#### **4. Machine learning and artificial intelligence negative**

Artificial intelligence (AI) allows computers to describe, understand, learn, reason, and integrate information to solve problems. AI simulates human intelligence so that better, quicker decisions can be made. AI is a fast-growing field utilized by many medical areas, enabling computers to gain human-like intelligence. For example, its applications to diabetes, a global pandemic, can change and improve the approach to diagnosis and management of diabetes. AI is useful in specialized CDSs for detecting diabetic retinopathy [36]. AI revolutionizes remote patient monitoring, continuously monitors the patient's symptoms and biomarkers, and adjusts to medicine and treatment in real-time, resulting in better clinical outcomes, including glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will reform conventional diabetes care by using a targeted data-driven approach and personalized care [37]. However, in regard to user attitudes, a survey study finds that negative perceptions of AI-based CDS tools may reduce staff excitement about AI technology [36]. Thus, it is important to have hands-on experience with AI so that users can gain more realistic expectations about the technology's capabilities.

Machine learning (ML) is a subset of AI. Machine learning features that machines can learn over time without being explicitly programmed. The ML algorithms include decision trees, random forests, artificial neural networks, genetic algorithms, and support vector machines. The ML algorithms have been used in building predictive risk models for diabetes or its consequent complications. For example, a webbased CDS can predict the early-stage risk of diabetes by classifying results using the patient's questionnaire without a testing kit. This CDS applies a deep learning approach resulting in better prediction accuracy than supervised machine learning [38]. Another study finds that fuzzy inference machines improve the quality of the day-by-day clinical care of diabetic patients and allow the remote monitoring of patients' clinical conditions, which helps to reduce hospitalizations [27].

Though AI seems to have unlimited possibilities, there are challenges to the adoption of diabetes AI devices, apps, and systems. Factors such as costs, user acceptance, physician cooperation, and interoperability between systems may affect how an innovation is adopted [39].

#### **5. Future care for diabetes**

Medical futurists predict there will be a cure for diabetes. A recent study on stem cells also concludes that beta cell replacement holds a promising cure for diabetes [36]. Biological and medical breakthroughs like the artificial pancreas, and glucose-responsive insulin, provide the correct insulin and the right time to patients. Regarding patient care in diabetes, virtual doctors, big data, data analytics, and social media, all these will become intertwined in the entire patient care ecosystem. Virtual doctors, a proof-of-concept CDS powered by an AI speech recognition system, are able to interact with patients and predict diabetes based on noninvasive sensors and deep neural networks [40]. Wearable technologies enable individualized monitoring of physiological variables in real time. The real-time data collected from multiple devices combined are fed into an artificial intelligence model using adaptive-neuro fuzzy interference to detect prediabetes and diabetes [41].

There is no doubt that digital transformation in healthcare will continue. Big data, machine learning, artificial intelligence, EHR-integrated, web-based, and mobile apps will improve, enhance, and adopt diabetes care. Medical and consumer devices collect a vast amount and variety of data, including continuous glucose monitoring data, insulin pump data, heart rate, hours of sleep, the number of steps walked, movement captured by wristbands or watches, hydration, geolocation, and barometric pressure. Next-generation developments of CDS will leverage big data and prioritize clinical actions based on data analysis, delivering maximum benefits to a given patient at the point of care. In the meantime, innovative care models and delivery methods will emerge. Personalized medication recommendations offered by CDSs fit each patient's insurance coverage, budget, lifestyle, and medicines. Outcomes can be analyzed constantly and regularly so that adjustments to medicines can be targeted based on the most recent patients' biological data.

Early diagnosis of diabetes and treatment will reduce the risk of developing comorbidity, delay the development of comorbidity, and improve quality of life for patients. CDSs facilitate doctors in clinical diagnosis and overcome clinical inertia in terms of prescribing habits. In addition, patient-centered care should consider patients' preferences in care decisions and identify effective methods to communicate CDS information to patients. Doctors need to be more tech-savvy in learning the latest technologies on patient care; patients want more empowerment by participating in self-care and care decision-making. Furthermore, increased number of diabetes journals publish AI-related technologies in diabetes care. Now, doctors must learn new skills and knowledge on AI tools, which have become part of diabetes health care [42].

A path forward may be computerized virtual coaches replacing human counseling; virtual doctors will be able to fully engage in the diagnosis, treatment, and continuous monitoring of chronic diseases. CDSs can be as good or superior to human doctors when prescribing diabetes medicines and may be more effective in overcoming clinical inertia as CDSs can remove human biases and habits. Considering the fact that the physician shortage is growing and 10.5% of the population has diabetes, CDSs play an important role in treating diabetes and more efficiently using clinical resources [43].

#### **6. Conclusion**

The CDSs for diabetes have improved significantly over the years. Multiple factors serve as driving forces for the uptake of CDSs. Newer technologies, initiatives, government mandates, and a competitive environment collectively facilitate advancement in diabetes care. This book chapter summarizes global CDSs development in recent years. Our review of the past few years' publications on CDSs for diabetes shows that the United States is leading the world in technology development and clinical evidence generation. Developing countries around the world are catching up in CDSs development and standards of patient care. The literature has consistently

*Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017… DOI: http://dx.doi.org/10.5772/intechopen.108509*

documented evidence of operational efficiency delivered by CDSs (e.g., reduced medical errors and reduced duplicate tests). The current evidence shows that both developing and industrialized countries have put more effort into AI and ML and will use artificial intelligence to their own advantage and innovative ways to develop more sophisticated diabetes CDS tools.

Though studies conducted 5 years prior commonly reported a low adoption rate of CDSs [4], recent publications show an increase in the adoption of CDSs, especially if CDSs are integrated into workflow and EHR. Our recent study of a quality improvement project using Glucose Pathway confirms this trend. In our project, the vendor has been working on integration with EHR. With the increased integration of CDSs with EHR, CDS adoption and utilization will significantly increase. CDS' true and long-term impact on outcomes, safety, and cost savings can be better measured and validated.

Advancements in technologies will continue to transform patient care, including doctors, processes, and patients. All entities in the patient engagement systems must learn, adapt, and adopt new developments to achieve better self-care, patient care, and clinical decisions. The future is bright but demands more learning on technologies.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Author details**

Xiaoni Zhang1,2 \*, Haoqiang Jiang1 and Gary Ozanich1

1 Northern Kentucky University, Highland Heights, KY, USA

2 The University of Alabama at Birmingham, Birmingham, AL, USA

\*Address all correspondence to: xzhang2@uab.edu

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[21] Larsen K, Akindele B, Head H, Evans R, Mehta P, Hlatky Q, et al. Developing a user-centered digital clinical decision support app for evidence-based medication recommendations for Type 2 diabetes mellitus: Prototype user testing and validation study. JMIR Human Factors. 2022;**9**(1):e33470. DOI: 10.2196/ 33470

[22] Lichtenegger KM, Aberer F, Tuca AC, Donsa K, Höll B, Schaupp L, et al. Safe and sufficient glycemic control by using a digital clinical decision support system for patients with type 2 diabetes in a routine setting on general hospital wards. Journal of Diabetes Science and

Technology. 2021;**15**(2):231-235. DOI: 10.1177/1932296820955243

[23] Tarumi S, Takeuchi W, Chalkidis G, Rodriguez-Loya S, Kuwata J, Flynn M, et al. Leveraging artificial intelligence to improve chronic disease care: Methods and application to pharmacotherapy decision support for type-2 diabetes mellitus. Methods of Information in Medicine. 2021;**60**(S 01):e32-e43. DOI: 10.1055/s-0041-1728757

[24] Khairat S, Marc D, Crosby W, Al SA. Reasons for physicians not adopting clinical decision support systems: Critical analysis. JMIR Medical Informatics. 2018;**6**(2):e8912. DOI: 10.2196/ medinform.8912

[25] Fico G, Hernanzez L, Cancela J, Dagliati A, Sacchi L, Martinez-Millana A, et al. What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project. BMC Medical Informatics and Decision Making. 2019;**19**(1):1-16. DOI: 10.1186/s12911-019-0887-8

[26] Laleci Erturkmen GB, Yuksel M, Sarigul B, Lindman P, Chen R, Zhao L, et al. Management of personalised guideline-driven care plans addressing the needs of multi-morbidity via clinical decision support services. International Journal of Integrated Care. 2018: University of Utrecht. DOI: 10.5334/ijic.s2132

[27] Ylenia C, Giovanni I, Lucia R, Donatella V, Tiziana S, Vincenzo G, et al. A clinical decision support system based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients. Mathematical Biosciences and Engineering. 2021;**18**(3):2654-2674. DOI: 10.3934/ mbe.2021135

[28] Delvaux N, Piessens V, Burghgraeve TD, Mamouris P, Vaes B, Stichele RV, et al. Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: The ELMO cluster randomized trial. Implementation Science. 2020;**15**(1):1-10. DOI: 10.1186/ s13012-020-01059-y

[29] Mahmoud AS, Alkhenizan A, Shafiq M, Alsoghayer S. The impact of the implementation of a clinical decision support system on the quality of healthcare services in a primary care setting. Journal of Family Medicine and Primary Care. 2020;**9**(12):6078. DOI: 10.4103/jfmpc.jfmpc\_1728\_20

[30] El-Sappagh S, Alonso JM, Ali F, Ali A, Jang J-H, Kwak K-S. An ontologybased interpretable fuzzy decision support system for diabetes diagnosis. IEEE Access. 2018;**6**:37371-37394. DOI: 10.1109/ACCESS.2018.2852004

[31] Abidi S, Vallis M, Piccinini-Vallis H, Imran SA, Abidi SSR. Diabetes-related behavior change knowledge transfer to primary care practitioners and patients: Implementation and evaluation of a digital health platform. JMIR Medical Informatics. 2018;**6**(2):e9629. DOI: 10.2196/medinform.9629

[32] Diabetes prevalence in Sri Lanka. 2021. Available from: https://data. worldbank.org/indicator/SH.STA. DIAB.ZS?end=2021&locations=LK&st art=2021. [Accessed: May 25, 2022]

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*Clinical Decision Support Systems for Diabetes Care: Evidence and Development between 2017… DOI: http://dx.doi.org/10.5772/intechopen.108509*

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#### **Chapter 8**

## Application of Systemic Accident Analysis (SAA) Approaches in Telemedicine/Telehealth

*Oseghale Igene and Aimee Ferguson*

#### **Abstract**

This chapter discusses the importance of applying methods based on the systems thinking paradigm in analysing accidents that may occur in a complex healthcare system involving telemedicine/telehealth. Different accident analysis approaches (models and methods) have been utilised to analyse incidents/accidents in different safety-critical domains, including healthcare, to identify weaknesses and to be able to propose safety recommendations. With the advent of systemic accident analysis (SAA) approaches based on the systems thinking paradigm, can they be feasibly and practically applied to incidents resulting from unintended issues relating to telemedicine/telehealth? This chapter discusses three popular SAA approaches, benefits and limitations, including their necessity for improving safety and even security relating to telemedicine processes.

**Keywords:** AcciMap, STAMP, safety, security, healthcare

#### **1. Introduction**

Telemedicine/telehealth are terms often used interchangeably to describe the use of digital technologies to provide healthcare services remotely [1]. It comprises a diverse collection of technologies (e.g., telephone, video, software, apps, instant messaging, email, online forms) and clinical applications (e.g., providing routine consultations remotely; monitoring patients at home; remote consultations, remote monitoring of symptoms, robotic surgery with a surgeon in another location). Most operational telemedicine processes/services that focus on diagnosis and clinical management remotely are carried out in industrialised countries (e.g., USA, Canada, United Kingdom, and Australia). Telemedicine has provided benefits for both patients and healthcare professionals. For patients, this includes better access to healthcare, including primary care (communication with GP and home monitoring) and secondary care (emergency specialist support, intra & inter-hospital access, shared case for diagnosis and treatment) [2], especially if they live rurally, and may be unable to attend to ill health or other commitments (e.g., work, caring). For healthcare professionals, telemedicine allows them to connect with other clinical specialists/ colleagues to assist with consultations regarding a patient. Telemedicine is also expected and helps to improve the quality of care, equity of healthcare access and

delivery efficiency in this regard [3]. The two main reasons telemedicine is utilised is because there is no alternative to this process, and it is considered better than conventional healthcare services [3].

Heinzeleman, Lugan and Kvedar noted in their paper that the future of telemedicine depended on three major aspects, including human factors, economic factors and technology [4]. Human factors comprise behaviours relating to how technology influences existing policies, culture, knowledge, and attitudes which can fundamentally affect changes at different levels in a complex socio-technical system like healthcare [4]. These levels consist of individual, organisational and societal levels. Individuals include patients who appreciate and expect high-quality, technology-enabled healthcare and healthcare providers (where their perceptions and behaviours are considered very important). At the organisational level, there is a focus on providing continuous care rather than episodic care and using less skilled and less costly providers as part of a multidisciplinary healthcare delivery approach. More critically, organisations support technology-enabled health care reflected in practices and policies regarding the future use of Information and Communications Technology (ICT). At the societal level, the acceptance of a patient-centred and technology-enabled healthcare delivery method is promoted. This promotion involves adapting new interventions (telemedicine) to create environments that reduce defensive medicine [4]. Although the concept of telemedicine is not a recent invention, the global COVID-19 pandemic was a catalyst for the widespread adoption of telemedicine in all areas of healthcare.

While considerable strides have been made regarding telemedicine and its impact on patient and community care, there is a need to proactively (and in some cases retrospectively) ensure patient and system safety, including the security of patient medical data and technologies. As earlier noted, despite the telemedicine processes being implemented quickly considering COVID-19, there is a need to consider if technologies are being utilised safely (e.g., This is very critical, especially when healthcare practitioners handle computing technologies, and while they help provide efficient healthcare, there is always a possibility of either human or software errors to occur. There have not been any studies exploring the application of systemic accident analysis approaches to telemedicine. This chapter explores this gap in addressing the importance of incorporating systems thinking and associated approaches to telemedicine in analysing potential incidents/accidents that may occur using systemic accident analysis (SAA) approaches. The proceeding sections will focus on elaborating three of the most popular SAA approaches applied across different safety-critical industries, including healthcare. Their applications, benefits and limitations will also be discussed.

#### **2. Systemic accident analysis (SAA) approaches**

Safety is considered one of the emergent properties of a complex socio-technical system, including the healthcare system [5]. This property is also considered to be very important because of the importance of ensuring the system safety and wellbeing of patients, professionals, and assets of a health organisation. Different analytical tools have been used to analyse risks and potential hazards that might occur, forming the process of incident/accident analysis [6]. These tools include the popular Root Cause Analysis (RCA) techniques, including Cause and Effect fishbone diagrams, the 5-Whys technique, Change Analysis and Barrier Analysis [7, 8]. However, these linear-based tools have been considered inadequate and unsuitable for analysing complex socio-technical systems [7, 9]. This realisation brought about the

#### *Application of Systemic Accident Analysis (SAA) Approaches in Telemedicine/Telehealth DOI: http://dx.doi.org/10.5772/intechopen.108660*

development of different systemic accident analysis (SAA) approaches, each based on various safety perspectives, methodologies and theories of accident causation [10, 11]. Some of the most popular examples of this type of approach include AcciMap (Accident Mapping) [12, 13], STAMP/STPA (Systems Theoretic Accident Model and Process) [14, 15], and FRAM (Functional Resonance Accident Model) [16, 17]. These approaches are considered more suitable for analysing incidents that are typically non-linear and involve complex causal relationships stemming from activities at the front line to decisions taken both with and outside health organisations [9]. They have been extensively applied in analysing major incidents within healthcare and other safety-critical domains [7, 18–21]. However, compared to other industries like Aviation, Railway, Nuclear, and Aerospace, the application of these approaches for incident investigation and analysis is still growing in the healthcare industry [7, 22]. These systemic approaches are further elaborated in the proceeding subsections.

#### **2.1 AcciMap mapping (AcciMap)**

Svedung and Rasmussen developed this approach as a graphical tool for creating a multi-causal diagram of events and decisions across different socio-technical levels, as shown in **Figure 1** [13, 18, 23–25]. This approach is also based on Rasmussen's theory of accident causation and can be applied either as a standalone method or as a part of a broader Risk Management Framework (RMF) [12, 23]. While there have been different variations of the AcciMap approach, Branford, in her thesis, developed a standardised AcciMap format with a set of guidelines for determining causal/contributing factors, causal relationships between them (linking causal connections within and between socio-technical levels) and formulating safety recommendations [23]. This systemic approach essentially provides the benefit of providing a graphical representation of actions/activities committed at the front end (where clinical practitioners and patients

are involved with patients using computing and network technologies) and latent conditions within the health organisation that may have facilitated the events to occur at the front end [21, 26]. Appendix A shows an example application of the AcciMap approach on a medication dosing error relating to a Computerised Order Entry System (CPOE) [21, 27, 28]. The AcciMap approach is based on the safety-I perspective, which essentially involves analysing what went wrong and why it happened so that recommendations can be made to prevent future occurrences.

#### **2.2 Systems theoretic accident modelling process (STAMP)**

STAMP is a systemic-based accident approach developed by Leveson (MIT). It is based on systems and control theory regarding safety constraints between various components and determining any disturbances that can potentially affect system safety [9, 15]. As shown in **Figure 2**, the STAMP model consists of a generic socio-

**Figure 2.** *Generic complex sociotechnical control structure (STAMP Model) [9].*

*Application of Systemic Accident Analysis (SAA) Approaches in Telemedicine/Telehealth DOI: http://dx.doi.org/10.5772/intechopen.108660*


#### **Table 1.**

*STAMP's control failure taxonomy of control flaws leading to hazards [14, 15].*

technical system safety control structure and a high-level taxonomy of safety constraints for system hazards. An example of the application of the STAMP approach for modelling a high-level control structure relating to the medication dosing error [21, 27] is shown in Appendix B. The STAMP approach also consists of two aspects of analysis; System Theoretic Process Analysis (STPA), which is a STAMP-based hazard analysis used for defining accidents, control structure and system hazards and Causal Analysis using System Theory (CAST) [15, 29]. This model is also based on the traditional safety-I perspective. **Table 1** describes the STAMP's control failure taxonomy of control flaws relating to how they lead to hazards in the system.

STAMP and AcciMap approaches are based on the traditional safety-I perspective, which considers "*safety as the absence of failure or the state in which the fewest number of things go wrong*" [30]. From this safety perspective, there is a shift in blaming the frontline level (actions/activities) to determining existing causal/contributing factors based on decisions at both organisational and external levels.

#### **2.3 Functional resonance accident method (FRAM)**

A systemic model was developed by Erik Hollnagel [31], and it's based on "Safety II" perspective that "*identifies and defines systems functions and variability determining*

#### **Figure 3.**

*The FRAM function and associated components [34].*

*how variability may interact within a system in a manner leading to adverse outcomes*" [16]. The development of this model type was motivated by the authors' dissatisfaction with existing approaches like Fault Tree Analysis (FTA) for addressing safety issues [32]. The FRAM model essentially relies on four principles, (a.) Equivalence of successes and failures where they both have the same origin (i.e., performance variability), (b.) Approximate adjustments where people and organisations continually adjust their performances to cope with daily operating challenges, (c.) Emergence, where identifying a sequence of events is considered impossible because many events are seen as emergent rather than a combination of conditions (i.e., latent), and (d.) Functional resonance representing signals coming from unintended interactions of variability (human, organisational and technical behaviours) of multiple signals [33]. A typical representation of the FRAM function is shown in **Figure 3**.

Each FRAM function consists of six [6] components which are briefly highlighted below:


This systemic approach, unlike the last two mentioned (safety-I perspective), is based on the safety-II perspective, which focuses on "*ensuring that as many things as possible go right*" in considering both accidents and outcomes [35]. Based on this safety *Application of Systemic Accident Analysis (SAA) Approaches in Telemedicine/Telehealth DOI: http://dx.doi.org/10.5772/intechopen.108660*

perspective, human beings are regarded as a resource necessary for system resilience and flexibility, especially when responding to varying conditions [35]. The safety-II view is also considered a proactive approach for anticipating events and allows clinicians' ability to adapt to pressures to be understood. Incident investigations using this perspective (applying the FRAM model) focus on how processes go right and serve as a basis for determining what went wrong [35, 36].

#### **3. Application of SAA approaches**

These systemic accident approaches elaborated in the previous section show their analysis methodology and safety perspective on which they are built for accident analysis. While these SAA approaches are graphically oriented in modelling interactions and relationships within a system, AcciMap and STAMP (STPA and CAST) focus mainly on determining weaknesses or loss of control so that safety recommendations can be developed to prevent their reoccurrence (Safety-I). These approaches differ from the FRAM approach, focusing on ensuring that things go right (Safety-II). Their steps regarding how each approach is applied in analysing an incident are highlighted.

#### **3.1 AcciMap analysis process**

Regarding applying the AcciMap approach to any incidents to analyse severe outcomes or near-misses, a set of guidelines was formally developed based on Branford's thesis [23, 24] after creating a standardised AcciMap format based on the original AcciMap structure. A graphic template is prepared, which consists of different AcciMap levels and includes the following processes:


#### **3.2. STAMP analysis process**

There are nine stages involved in applying STAMP, as identified by Leveson [19]. The first eight stages can be carried out, not necessarily in a strict order. When specifically using the Causal Analysis, which is based on STAMP, these stages are summarised as follows:


#### **3.3 FRAM analysis process**

Applying the FRAM method allows positive and negative consequences from work adjustments rather than focusing on causes or contributing factors [37]. There are four processes involved in analysing and developing a FRAM model as follows:


#### **4. Discussion**

Each of the SAA approaches addressed in this chapter has been grouped into nine [9] specific categories according to authors Karanikas and Roelen to distinguish them from one another based on their strengths and weaknesses [38]. **Table 2** indicates the SAA approaches strengths (green) and limitations (orange). The subsections will further elaborate on the benefits, demerits and necessity of applying these systemic approaches.


*Application of Systemic Accident Analysis (SAA) Approaches in Telemedicine/Telehealth DOI: http://dx.doi.org/10.5772/intechopen.108660*


**Table 2.**

*Strengths and weaknesses of AcciMap, STAMP and FRAM [38].*

#### **4.1 Benefits of SAA approaches**

Application and comparative studies across different safety-critical industries have highlighted the benefits of applying systemic accident approaches compared to the linear-based approaches in their ability to graphically highlights weaknesses in complex systems and how these systemic issues can create scenarios where actions/activities are committed that could potentially lead to patient harm. The health industry can only continually reap the benefits of using these approaches for incident analysis. Each approach will be further elaborated in specifically examining the benefits of SAA approaches already highlighted in Section 2. For the AcciMap method, its ability to graphically present causal relationships that exist between multiple causal/contributing factors will allow analysts to not only determine causal flows within different socio-technical levels but will also let systemic issues be traced back to the higher levels (organisational and external) and to determine any existing policies and processes that need to be reviewed. The AcciMap approach can be applied by a single analyst but has more benefits when used by a group of analysts (especially having different specialisations within healthcare). The latter process will help foster brainstorming when analysing incidents and producing the initial AcciMap output to the final result after several iterations. Hypotheses can also be drawn based on the AcciMap analysis, and counterfactual reasoning can also be applied when considering sufficient and necessary causes. The STAMP model allows analysts to portray feedback loops between different components within the system graphically. These feedback loops include communication between practitioners and patients (remote connections), as well as with higher bodies and determine any loss of control. Based on the outputs (Appendices A and B), applying both AcciMap and STAMP approaches was used to illustrate and determine systemic factors and any loss of control between system components using a conventional medical scenario. If these approaches were to be applied in a telemedicine scenario, the links between the patient and medical practitioners (communication) would have to be considered. These analyses will also include issues associated with computing technologies applied remotely for communication or administration. When using the AcciMap method, these aspects can be analysed at the physical/actor level to determine if there was any miscommunication between staff and patient, defects in technology used, and what conditions precipitated it. The STAMP model can also be applied in a telemedical scenario when feedback loops can be analysed to determine any loss of control when looking into issues

*Application of Systemic Accident Analysis (SAA) Approaches in Telemedicine/Telehealth DOI: http://dx.doi.org/10.5772/intechopen.108660*

regarding remote administration and communication (or lack of) between patients and staff. The FRAM model can also be applied in a telemedical situation. However, compared to conventional medicine, the difference is that aspects regarding how technology is "remotely" implemented in communicating and providing patient care will need to be analysed if there are any accidents, near-misses or loss of control. Other aspects can also include patient or medical staff misapplication and accessibility of technology.

#### **4.2 Demerits of SAA approaches**

It is also important that while there are benefits to applying systemic approaches, the demerits must also be highlighted. Major drawbacks of using systemic models for incident analysis as applied to telemedicine are resources required in terms of personnel, time, and knowledge needed to apply them effectively. Depending on the approach used, it can take considerable time and effort to understand the causation theory and methodology behind each approach and to apply them to analyse any major incident(s) in healthcare. Also, about this point, it is very important to take into consideration when it comes to each systemic approach's validity, reliability and usability in producing results that will allow effective safety recommendations to be formulated (retrospectively) and that these safety measures can then be tracked and assessed to ensure that any weaknesses detected will not occur again. While each approach can be applied individually, as each analytical iteration can be reviewed, it is usually recommended that multiple users (team-based) apply the approach to the same incident. This step will allow brainstorming and discussions to produce the final outcome. Health organisations will also require training of staff associated with risk management and computing technology in applying different systemic approaches, which could also take a considerable amount of time. These points highlight why Root Cause Analysis (RCA) techniques are still being used because they do not require as much time in terms of training and application. Still, as stated earlier in this chapter, their underlying methodologies are not considered suitable for analysing complex systems. However, this limitation is somewhat circumvented when using softwarebased modelling tools based on some systemic approaches. For instance, there are a FRAM visualiser and STAMP Workbench applications for FRAM and STAMP/STPA analyses, respectively. Microsoft Visio application or other graphic tools can construct AcciMap outputs during analysis.

#### **4.3 The Necessity of SAA approaches**

Considering the benefits and demerits discussed in previous subsections, it stands to reason that health organisations will need to weigh these benefits versus the limitations of applying these approaches for incident analysis relating to telemedicine. Based on previous studies comparing systemic approaches with other linear-based and systemic-based models [7, 11, 21], there is a clear conclusion that applying the systems thinking paradigm is the way forward in analysing, understanding, and improving system safety relating to telemedicine. This point also encompasses processes that are involved when it comes to telemedicine as far as healthcare professionals and patients are concerned. It is very important to acknowledge the general limitations of these approaches mentioned and understand that there are tangible benefits depending on which systemic approach is implemented. While there have been studies investigating how the "*research-practice gap*" as coined by Underwood and Waterson [39], can be

reduced in terms of applying these approaches in healthcare, there is still a need for providing awareness to clinical safety and risk managers. Aside from the need to improve system safety by ensuring that whatever safety recommendations or mitigating processes are set in place relating to telemedicine processes, there is also a need to protect patient medical data from hacking and other breaches. This issue of patient data being hacked relates to cyber security, especially when the connection is over an unencrypted or public network [40]. The STAMP model can also be applied to analyse this type of security-related incident by implementing an STPA analysis specifically for cyber security analysis called STPA-SafeSec (System-Theoretic Process Analysis for Safety and Security) [41–43]. The authors added that STPA could be applied to analyse system safety and security and systems regarding emerging properties. Security analysis using STPA-Sec serves as a means of ensuring the safety of patient medical data for telemedicine, according to Young and Leveson. They also indicated that safety and security must be addressed collectively [41, 42].

#### **5. Conclusions**

There is a need to consider the importance of incident analysis relating to telemedicine to improve practices/processes and ultimately improve safety and security relating to patients, medical data, health professionals and health organisations. Applying systems thinking by utilising systemic approaches will help to graphically model interactions in complex socio-technical systems and detect weaknesses by examining causal/contributing factors, causal relationships, and any communication and feedback loops within the system. However, applying these approaches requires considerable resources regarding awareness, training, and proper application of guidelines to realise their necessary benefits and improve the process of telemedicine.

### **Acknowledgements**

There is no funding information relating to the work of this chapter.

#### **Conflict of interest**

There are no conflicts of interest relating to the contributions for this chapter from the authors.

#### **Appendix A: AcciMap Analysis of the Medication Dosing Error related to the CPOE system**

### **Appendix B: The STAMP Control Structure of the CPOE Medication Error Case Study**

### **Author details**

Oseghale Igene<sup>1</sup> \* and Aimee Ferguson<sup>2</sup>

1 University of Glasgow, Glasgow, United Kingdom

2 University of Strathclyde, Glasgow, United Kingdom

\*Address all correspondence to: oseigene@hotmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Application of Systemic Accident Analysis (SAA) Approaches in Telemedicine/Telehealth DOI: http://dx.doi.org/10.5772/intechopen.108660*

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[23] Branford K. An Investigation into the Validity and Reliability of the AcciMap Approach. Australia: The Australian National University; 2007

[24] Branford K, Naikar N, Hopkins A. Guidelines for AcciMap analysis. In: Hopkins A, editor. Learn from High Reliab Organ Sydney CCH. 2009. pp. 193-212

[25] Lee S, Moh YB, Tabibzadeh M, Meshkati N. Applying the AcciMap methodology to investigate the tragic Sewol Ferry accident in South Korea. Applied Ergonomics. 2017;**59**:517-525

[26] Igene OO, Johnson CW, Long J. An evaluation of the formalised AcciMap approach for accident analysis in healthcare. Cognition, Technology & Work. 2021:1-21

[27] Horsky J, Kuperman GJ, Patel VL. Comprehensive analysis of a medication dosing error related to CPOE. Journal of the American Medical Informatics Association. 2005;**12**(4): 377-382

[28] Igene OO, Johnson CW. Comparing HFACS and AcciMaps in a health informatics case study - The analysis of a medication dosing error. In: Safety and Reliability - Safe Societies in a Changing World. ESREL 2018: Proceedings of the 28th International European Safety and Reliability Conference; 2018. pp. 3-10

[29] Leveson N, Daouk M, Dulac N, Marais K. Applying STAMP in accident analysis. NASA Conference. 2003: 177-198. Available from: https://ntrs.nasa. gov/api/citations/20030111708/down loads/20030111708.pdf

[30] Smaggus A. Safety-I, Safety-II and Burnout: How Complexity Science Can Help Clinician Wellness. Vol. 28. BMJ Quality and Safety. BMJ Publishing Group; 2019. pp. 667-671

[31] Hollnagel E. Barriers and Accident Prevention. London, Aldershot, UK: Ashgate; 2004

[32] Grant E, Salmon PM, Stevens NJ, Goode N, Read GJ. Back to the Future: What do Accident Causation Models tell us About Accident Prediction? Vol. 104. Safety Science. Elsevier B.V; 2018. pp. 99-109

[33] Riccardo P, Gianluca DP, Giulio DG, Francesco C. FRAM for Systemic

*Application of Systemic Accident Analysis (SAA) Approaches in Telemedicine/Telehealth DOI: http://dx.doi.org/10.5772/intechopen.108660*

accident analysis: A matrix representation of functional resonance. International Journal of Reliability, Quality and Safety Engineering. 2018 Feb;**25**(1):1, 1-29

[34] Flin R, Winter J, Cakil Sarac MR. Human factors in patient safety: Review of topics and tools. World Health. 2009;**2**

[35] Hollnagel E, Wears R, Braithwaite J. From safety-i to safety-II: A white paper from safety-I to safety-II: A white paper. In: Safety-II: A White Paper From Safety-I to Safety-II: A White. 2015: 1-43. DOI: 10.13140/RG.2.1.4051. 5282.o

[36] Finkel M. On Flexibility: Recovery from Technological and Doctrinal Surprise on the Battlefield. Stanford, CA: Stanford University Press; 2011

[37] Hollnagel E, Hounsgaard J, Colligan L. FRAM-the Functional Resonance Analysis Method-a Handbook for the Practical Use of the Method. 2014. p. 75

[38] Karanikas N, Roelen A. The Concept Towards a Standard Safety Model (STASAM). MATEC Web Conf; 2019

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**Chapter 9**

## Addressing Pain Points: Thinking outside the Telehealth Box

*Lua Perimal-Lewis, Patricia A.H. Williams, Ginger Mudd and Gihan Gunasekara*

#### **Abstract**

In this chapter, we present the synthesis of six pain points relating to Australia's hospital congestion which is under crisis. The COVID-19 pandemic forced health services to respond rapidly to maintain continuity of care through telehealth. Some of these strategies were anticipated to be short-term arrangements, implemented quickly, and haphazardly deployed. While the health emergency accelerated the adoption of telehealth and models of remote care, this implementation was reactive. It is evident that our hospital systems continue to grapple with the issues of an aging population, expanding demand for mental health services, and escalating costs and too few resources. A shift in philosophy to address these and other recurring pain points presents opportunities to embrace virtual care beyond current implementations of telehealth.

**Keywords:** telehealth, virtual care, healthcare pain point, hospital congestion, pandemic

#### **1. Introduction**

It was a leisurely Sunday afternoon in a small country town with children playing happily until a freak accident on the playground "hamster wheel." A possible fractured finger and a trip to the Emergency Department (ED). Calling ahead to notify the ED of our arrival, the nurse replied, *"Good timing, I was about to head off!"*. The rural ED with a quaint cottage like frontage (complete with doorbell) revealed an empty waiting room. The nurse greeted us and returned with a pile of paper-based forms. Apparently, the doctor on-call would arrive shortly. She joked upon seeing a 30-year-old man, *"I was expecting to see a four-year-old!"*. She confirmed a fracture but advised that she is not able to treat the injury until they could find the radiologist. Since there were no other patients in the waiting room, she went home to feed her 2-year-old. The radiologist arrived after 30 minutes. Within minutes the ED became busy with other presentations; (A) an older patient recently discharged from hospital arrived by an ambulance, (B) a worried young father holding a baby with gastro, (C) a young man with a drug overdose and mental health condition, and (D) another older patient with an allergic reaction to his eyes after tending to the weeds in his garden. The doctor returned to an (E) overcrowded ED but was calm and collected and

#### *Telehealth and Telemedicine - The Far-Reaching Medicine for Everyone and Everywhere*

#### **Figure 1.**

*Australia's recurring healthcare challenges [1, 2].*

did a stellar job in attending to all the patients. Despite the chaos, the lack of privacy, the uncertainty, and the frustration of waiting, all the patients were cared for with the limited resources.

As for the fractured finger, the doctor communicated via mobile phone with consultants at a metro hospital, located hundreds of kilometeres away. The X-ray was sent via WhatsApp to the senior consultant and to us (F). Eventually, we left with a taped splint and painkillers.

This real ED experience highlights the issues and pain points (see **Figure 1**) that clinicians at acute care setting have to deal with on a daily basis, and at scale for busy metropolitan hospitals. These recurring issues impact patients, healthcare providers, and the government. Worldwide, such pain points are worsening demanding that hospital administrators and policymakers understand the complexity of these multilevel interactions and look outside of the acute care settings to improve patient outcomes.

This chapter discusses how telehealth and the evolution to virtual care can begin to address the major pain points faced by overburdened hospitals as well as enhance the patient experience.

#### **2. The evolution of telehealth to virtual care?**

Virtual care is a broad term for all the digital tools and real-time communication, to enable healthcare providers to remotely work together with the patient [1]. In Australia, like many countries, health services are stretched and faced with an increasing burden from an aging population, rising costs, and increasing demand

*Addressing Pain Points: Thinking outside the Telehealth Box DOI: http://dx.doi.org/10.5772/intechopen.108659*

(**Figure 1**). Telehealth was vastly underutilized prior to the COVID-19 pandemic. The global response prompted a broad and reactive adoption of telehealth. In Australia, there was a 35% increase in use over only a 2-month period in early 2020 [3]. Greater increases were seen worldwide. In addition, research has shown that consumer acceptance of telehealth has been high and a predominantly positive experience [4].

While telehealth can be defined as the use of telecommunication techniques for the purpose of providing telemedicine, medical education and health education over distance [5, 6], and telemedicine uses advanced technologies [5, 7], virtual care refers to the delivery of patient-centered, cost-effective, and timely clinical care from a distance such as real-time video interaction and online exchange of information between a patient and their doctor and/or clinical team [8]. A wide perspective is needed to rethink delivery of healthcare that ensures continuity of care across multiple providers that can be delivered outside of acute care hospital environments.

As technology, in particular home and remote monitoring devices, evolve, the opportunity to support remote care using technology has increased. In Australia, this is now coined as virtual care to characterize a more complete experience for the patient, rather than telehealth consultation which can be anything from a 5-minute telephone call to a video consultation. It is unfortunate that because of the rapid deployment during COVID-19, the medical interaction was often second to the management and inexperience with the technology. As patients and clinicians have become more familiar with the technology, there is an opportunity to support consultations with home monitoring devices.

The following sections briefly discuss these pain points by giving select evidence where the underlying solution is outside of the acute care settings.

#### **2.1 Aging**

Most older patients presenting at acute care settings have multiple chronic conditions and a higher risk of hospital readmission for complications attributed to a different primary condition to the indexed admission [9]. Therefore, recently discharged older patients with chronic health conditions require a well-coordinated solution at home. Simulating a hospital environment, these patients can be effectively managed at home using remote patient monitoring devices or wearables that continuously monitor the patient's condition by tracking vital signs and medication compliance. The data collected is sent to the clinicians allowing them to monitor, identify symptoms, and intervene early. Reducing hospitalization will benefit older patients who are more likely to be affected by adverse drug events [10] and hospital acquired infections which prolong Length of Stay. The implementation and sustainability of virtual care for older adults is contingent on better integration of technology into their lives as well as healthcare providers [11]. Although still in its early stages, experience shows that technologies used in hospital at home are safe and acceptable to both patients and clinicians, as well as it reduces the ongoing resource tension at acute care settings [12].

#### **2.2 Patient experience**

Healthcare organizations are striving to provide patient-centered care, yet a long way from addressing the most prevalent patient pain points such as care fragmentation, long waiting times, poor doctor-patient communication, and poor logistics in hospitals, all of which contribute to compromised care and services that are not

personalized to the individual [13]. Analysis of patient satisfaction has been part of healthcare service review for a number of years; however, there is a move from satisfaction to patient experience which is a proxy for one of the six indicators of quality healthcare in the US [14]. The "young father and the baby with gastro" is a classic example of care fragmentation. It was evident when the ED doctor attended to this patient, that the baby has other chronic illness managed by other services. The patient has navigated the health system by moving from one health service to another, and there was lack of communication between the various service providers. Communication is a key factor in measuring patient experience [15, 16]. Fragmentation across continuum of care is common because of the very nature of a patient's journey through disparate systems, availability of medical data, and lack of real-time data sharing. It is the development of secure and scalable digital infrastructure that is the foundation to facilitate a coherent patient journey by moving to an increasingly connected and integrated healthcare system.

#### **2.3 Mental healthcare**

Another pain point for the government is the management of mental health patients who tend to have longer wait times in ED, often requiring more time for stabilization and/or to complete investigations and are more likely to present after hours, suggesting the need for community-based services [17]. Reconceptualizing mental health service delivery requires organizations to traverse beyond telehealth; mental health professionals need to embrace smartphone-based digital technology such as Ecological Momentary Assessment (EMA) for continuous monitoring and early intervention outside of acute care, so on demand care can be offered before an adverse event. The "young man with drug overdose and mental health issues" was a regular visitor and could have been better managed outside of ED using EMA. Embracing digital health technology would enable service providers to transition care outside of health facilities by means of continuous digital touchpoints enabling irregularities in behaviors identified in real time, alerting a crisis. A well-implemented virtual care platform can enable continuous data collection from active monitoring and passive sensor data to deliver personalized and timely intervention.

#### **2.4 Costs**

The Australian healthcare funding model is complex in nature, funded by all levels of government—federal, state, territory, and local. Virtual care service provision, enabling care outside of acute care settings, will drive innovation in funding as demonstrated during COVID-19, but more is needed. The cost savings gained by keeping patients out of hospitals could be diverted for the setup of virtual care infrastructure, improving primary care funding and extending Medicare supported service provision by allied health professionals. The "patient presenting with allergic reaction to his eyes" could be better managed at a primary care health facility, which was nonexistent in the small country town. Innovative funding model to find synergies on how the government fund primary care will be the driving factor to improve poor health outcomes in rural and remote communities, with less reliance on larger urban centeres. The ED doctor who had to communicate with consultants at a metro hospital is a case in point. In addition, it is vital that all communication regarding a patient is through a secure platform rather than via WhatsApp which poses unprecedented data sharing noncompliance risk. Prioritizing funding to finance secure platforms with the

agility of WhatsApp that ensures confidentiality, privacy of patient, and clinical data should dominate federal healthcare funding conversation.

#### **2.5 Emergency department**

Waiting times is a key issue in Eds, with hospitals under pressure to meet National Emergency Access Target targets [18]. Enabling virtual care assessment, management of low acuity patients, and devising innovative ways to empower primary care physicians to keep patients out of hospital would reduce ED presentations, waiting times, and ambulance ramping. Investment into processes and identifying care pathways that can triage patients that do not need hospitalization such as the vast majority of psychiatric patients by offering appropriate interventions and connection to community/primary care would naturally reduce the pressure on ED and would allow emergency resources to be better utilized for more serious cases.

#### **2.6 Primary care**

Access to health services remains a problem for residents of isolated settlements [19]. Virtual care is a vital component to addressing this issue. While "telehealth" and "telemedicine" have been widely used in Australia over the past decade as a means of overcoming problems of access to healthcare and the shortage of health professionals in rural and remote areas [20]; in many cases, telemedicine and telehealth are used to augment other service delivery models [19]. There is a need to ensure a comprehensive range of well-coordinated primary healthcare and specialist services are accessible locally and virtual care should be part of this solution, particularly as the prevalence of chronic disease grows with the aging of Australia's rural and remote population [19]. A well-coordinated, technology-enabled acute to primary and specialist care pathways will streamline the process of follow-ups post discharge.

#### **3. Discussion**

Sustainable and effective virtual care provision, with telehealth as part of this, is the way forward for healthcare in Australia. Creating a virtual care environment

**Figure 2.** *Virtual care components [1, 2].*

means looking at the pain points more broadly from the perspective of how a care team rather than acute services can address the challenges. These solutions must be cognizant of the patient and healthcare provider user experience, fit into clinical workflows and care processes, and supported by the integration of technologies with information systems underpinned by seamless communication. They must also be interoperable, scalable, and secure (**Figure 2**).

These components allow the collation of technologies to generate accessing, sharing, coordinating operations, and security capabilities. These capabilities facilitate the interface between people and integration of information with care processes.

In addition to the virtual care components, there are several actions needed to support the ongoing and increased adoption of virtual care and telehealth in Australia and across the world. Funding and reimbursement for services both for consumers and providers, and investment in infrastructure to support service delivery are issues that need attention given that 7 million people (28% of the population) live in rural


#### **Table 1.**

*Examples of the types of devices that have monitoring and diagnostic functions and their application.*

#### *Addressing Pain Points: Thinking outside the Telehealth Box DOI: http://dx.doi.org/10.5772/intechopen.108659*

and remote areas in Australia [21]. Another challenge is the integration of virtual care services into routine care and clinical workflows. In Australia, many telehealth consultations were via the telephone only and did not result in optimal healthcare delivery [22]. This in turn also requires a competent workforce to utilize virtual care and new or adopted models of care. As virtual care is dependent of the use technology, there is a need to invest in support structures and infrastructure which includes an ecosystem that supports wearable technology.

Wearable technology can be worn on the body or implanted. They collect data directly from the person and communicate it with other connected devices such as a smartphone or computer, using Bluetooth or the Internet. Digital natives are likely to use these wearable devices without much hesitation, but digital immigrants may need some persuasion. However, a recent study found that older adults, especially those who are familiar with smartphones, are interested in using wearable devices and joining online health community enabling aging in place [23]. **Table 1** lists examples of the types of devices that have monitoring and diagnostic functions and their application.

Like aging in place philosophy, a shift to deliver care in place could see select emergency care generally offered in the ED are taken to patients triaged suitable to receive such care. Patients could be assessed in the comfort of their environment by paramedics connected to emergency physicians located at the hospital through a secure video link. Once stabilized, patients could be left with a remote monitoring device which will continue to send life feed to the clinicians at the hospital who can intervene in an event of health deterioration. The remote monitoring and care can continue until the patient can be safely discharged to primary care services in the community, preventing repeat hospital visits or admission soon after discharge like the older patient who presented at the ED described in the scenario above. The availability of data on the electronic consultation as well as the availability of remote monitoring data can facilitate better informed clinical decision-making based on the trend of the collected data. Such data availability is almost nonexistent in the traditional modality. For an emergency physician who is normally exposed to a chaotic ED environment, a virtual care modality where that care is delivered virtually from the comfort of their home would aid better judgment, thus improving patient outcome and contributing to a less stressed workforce.

#### **4. Conclusions**

In the ED presentations described here, there are many opportunities which can be explored using virtual care that could have prevented almost all of these presentations. There is no framework for how to move forward with virtual care in a comprehensive manner in Australia [24, 25]. Without an inclusive and all-encompassing framework for virtual care adoption, the facets it comprises, its challenges and what benefits it can realize, it is not possible to innovate and devise solutions to address these patient and government pain points.

It is the combination of people, monitoring and communication technology, process, infrastructure, information, and policy that form the basis for the future for virtual care solutions to become part of the common practice. Ultimately, it is the benefits for the patient, healthcare providers, and healthcare system that virtual care can deliver that will drive the medium- and long-term adoption. Virtual care has the potential to reform healthcare, providing patient-centered care with more convenience, less costs, and greater productivity.

On a national basis, governments need to devise clear roadmaps for digital health innovation in virtual care to reduce costs and improve accessibility and health outcomes, while solving the acute care sector pain points. Funding, leadership, and policy are key success factors that will strengthen its adoption to realize improvements in patient outcomes. For rural and remote communities in particular, research into the efficacy of remote monitoring and wearable device collected data to support patient-centered healthcare decision-making and patient outcomes will contribute to the introduction of outcomes-based funding models.

From an industry perspective, upskilling the workforce to deliver virtual care services as well as new integrated models of care will help meet the increasing demand for services. Future opportunities using augmented and virtual reality open up new avenues for education of the healthcare workforce in innovative ways.

Ultimately, awareness of virtual care by patients and consumers increased due to COVID-19. The rapid adoption of the technology, arguably not well thought out in the rush, needs to be reassessed to focus on the improvement and reliability and transparency, so that the focus can be on the clinical iteration and patient outcomes rather than the technology used to achieve the communication.

A bold vision needs to emerge for an information and technology enabled healthcare system, where care is virtualized and enabled where it is needed, rather than where it is available. It is a system where clinicians and patients can communicate effectively and one in which clinical process is centered around the needs of the patient. Most importantly, it is an environment in which the patient is better engaged and supported in their journey to wellness. Concerted efforts are needed to systematically, rigorously test and evaluate the impact of various modality of care virtualization to address healthcare pain points.

#### **Acknowledgements**

Cisco Systems Australia is acknowledged for their kind finding contribution to the research project in virtual care.

#### **Conflict of interest**

The authors declare no conflict of interest.

*Addressing Pain Points: Thinking outside the Telehealth Box DOI: http://dx.doi.org/10.5772/intechopen.108659*

#### **Author details**

Lua Perimal-Lewis\*, Patricia A.H. Williams, Ginger Mudd and Gihan Gunasekara Cisco-Flinders Digital Health Design Lab, Flinders University of South Australia, Adelaide, Australia

\*Address all correspondence to: lua.perimal-lewis@flinders.edu.au

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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### *Edited by Tang-Chuan Wang*

Telehealth includes clinical and non-clinical services, such as monitoring, diagnosis, and treatment, as well as delivery of preventative care and health promotion. Telemedicine is a more common term that describes remote clinical services. The major impact of COVID-19 has caused telemedicine to become mainstream since the end of 2019. Clinicians are having to use tele-consultation, tele-monitoring, telepharmacy, tele-rehabilitation, tele-surgery and other remote methods and technologies to assess and treat patients. In patient-centric telehealth/telemedicine, personalized data generated by the various monitoring technologies are jointly managed by patients and medical staff, moving us ever closer to the ideal of precision medicine. With contributions from many respected authors, this book incorporates updated developments as well as future prospects for the ever-expanding field of telehealth/ telemedicine. It can also serve as a reference for anyone involved in this field, whether they are clinicians, researchers or patients.

### *Robert Koprowski, Biomedical Engineering Series Editor*

Published in London, UK © 2023 IntechOpen © blackdovfx / iStock

Telehealth and Telemedicine - The Far-Reaching Medicine for Everyone and Everywhere

IntechOpen Series

Biomedical Engineering, Volume 16

Telehealth and Telemedicine

The Far-Reaching Medicine for Everyone

and Everywhere

*Edited by Tang-Chuan Wang*