The field of health informatics is rapidly evolving to encompass data from outside health care systems. Much of the external data is at the community level and is analyzed in conjunction with patient-level data to identify potential living circumstances that adversely affect patients’ health. These circumstances, or social determinants of health, are often the key to resolving chronic health problems and preventable use of health care facilities. The effective resolution of these problems frequently requires a partnership between health professionals and others, such as attorneys, social workers, or civil agencies. The exponential growth of data that are available presents challenges in management, secure storage, and translation into actionable knowledge that facilitates gained insights in a timely manner, and recruitment and retention of skilled personnel.KeywordsInformaticsElectronic Health Records (EHR)DataDatabaseSocial determinants of health (SDOH)DisparitiesIndicators

In the clinical setting, the focus is on individual patients after they become sick or injured. In contrast, public and population health focus on groups of patients or the public at large. These groups can be as small as a local neighborhood, or as big as an entire country, region, or the world. Instead of diagnosis and treatment of individuals, the goal is prevention of disease, identification of risk or protective factors, or promotion of the health of communities. These missions make them attractive use cases for FHIR and FHIR Bulk Data Access. We will begin this chapter with a discussion of public health in which we will encounter some particularly sophisticated applications of FHIR and then shift to population health.KeywordsPublic healthPopulation healthClinical quality measures (CQMs)Genomic screeningClinical guidelinesElectronic case reporting

This chapter describes clinical needs, foundation for clinical alerting and surveillance, It started with historical perspective and outlining public health needs. Emergency services provide rapid response for deteriorating patients that use number of assessments tools. Emerging Clinical Decision Support Systems moved to more advanced systems that required better evaluation and adoption. This chapter provides foundation for understanding clinical impact. At the end of the chapter, there is brief case study of Clinical Control Tower.KeywordsAlertsSurveillanceElectronic Medical RecordControl Tower

Abstract The use of Artificial Intelligence (AI), Machine Learning (ML) and advanced analytics can yield important contributions to our understanding of how current systems and practices contribute to health disparities. They can also inform the development of equitable interventions, policies and decision-making in clinical care. This can only happen if we understand and address the biases in our healthcare system today, and how they are reflected in the data we use to develop and train AI systems. In this chapter, we will provide an overview of healthcare data sources and describe the ways in which the different types of data can be biased. We will discuss the impact of biased data, citing specific examples of how biased data has led to erroneous results or decisions, with particular focus on health equity and disparities. We will then describe strategies and techniques to both improve data prospectively, and to mitigate biases in how we use and interpret existing data to inform decision-making in healthcare.KeywordsArtificial intelligence (AI)Machine learning (ML)Data biasHealth equityReal world data (RWD)Real world evidence (RWE)

In this chapter, we begin a discussion of the key informatics tools needed to power a Learning Health System. These include electronic records for both providers and patients as well as information sharing technologies to bind them together. There are two similar terms used to describe these records. Electronic medical records (EMRs) are best thought of as “the standard medical and clinical data gathered in one provider’s office”. In the early days of clinical information systems, before data sharing was feasible creating the EMR, as defined, was the goal. The newer term is electronic health record (EHR) and it contains a more complete record of a patient’s care. Increasingly, with the growth of mHealth (mobile health), it may also contain data contributed by the patients using mobile apps or wearable or other devices in the home.KeywordsEHR challengesHITECHEHR certificationMeaningful usePhysician EHR satisfactionEHR innovations

IT applications in a healthcare organization should be designed in such a way that the information documented by healthcare professionals can be interpreted and processed for a specific purpose. At the same time, the data from the electronic medical record can be used “automatically” in the secondary processes of the healthcare organizations, for example for accounting or for staff planning purposes. In this type of application, the healthcare professionals at the point of care need to collect the data only once, thus eliminating the need for additional documentation time, which is often perceived as pointlessly “administrative” or “bureaucratic”. As healthcare professionals are involved, management decisions can be based on information that is more accurate and derived directly from the primary process.In accordance with the guiding principle “collect once, use many times”, the focus must be on fully exploiting, for management purposes, the current software options with regard to the electronic medical record, use suitable coding systems such as classification or nomenclatures for documentation entries, and comply with international standards for the use of clinical data for secondary purposes. The aim is to reduce additional documentation time at the point of care while making the best possible use of the data for secondary processes.KeywordsWorkforce managementHealth professionalsProcess interoperabilityData accuracyCoding systemInternational standards

A hybrid health model that integrates digital and physical care has a broad impact on our healthcare system and beyond. In this chapter, we will use the Apple Heart Study as an anchor example to analyse the various strategic implications of the hybrid health model. Based on the foundational knowledge and analysis of various technologies from previous chapters, we aimed at expanding our understanding of the potential impact on various participants in healthcare and society at large. With the continuous development of technologies and the application in health, we will see a range of new combinations of hybrid delivery models. We will focus on four areas that these innovations could change how we design and deliver care, namely the evolutions in human-machine, human-human, life-heath, and individual-public interfaces. We will also present an integrative framework to help our readers navigate these evolving interfaces of the health system.The unleash of data can be powerful in changing our healthcare landscape and its integration with society. It may present new opportunities to improve health outcomes and reduce costs. At the same time, it may also present threats to our existing system and even introduce new risks, such as privacy and security. Importantly for all players in the health system, from patient to practitioners and from private to public sectors, we need to continuously reflect on our actions and determine our strategic actions on this transformational journey.KeywordsHybrid healthStrategic impactsStrategic implicationsSocietal impactsDigital twin(s)Apple heart studyDigital wearablesHuman-machine InterfaceHuman-human InterfaceLife-health InterfaceIndividual-public Interface

Abstract The availability of wearable devices recently boosted the popularity of self-tracking technologies. Self-trackers are involved in a complex process of knowledge development, but this cannot be achieved without knowing the body. However, self-tracking devices seem to embrace an abstract and scattered conception of the body, based on unrelated numbers, graphs, and depictions, which may not be integrated into a coherent body image. This may turn into biases and distortions of how we look at our bodies, worsening, rather than improving, our self-knowledge. In this chapter we explore the ways through which the progressive “quantification” introduced by self-tracking technologies is affecting the body. We first explain a series of theoretical constructs concerning the body, which are essential to understand the impact of self-tracking on our bodies, like body schema, body image, and bodyawareness. Then, we illustrate how individuals’ body image and awareness are affected by the usage of self-tracking technologies in the sports domain. Finally, we point out some lines of future research aimed at providing people with more meaningful representations of their own body, improving their body awareness and even their body image.

mHealth is a huge market that provides users the opportunity to have better health and healthcare quality. Health apps support citizen’s empowerment through self-management, health promotion, disease prevention, providing personalized health advice and care. However, the rapid development of the mHealth sector raises concerns about the potential risk of health functions apps providing transmission of health data, the capture of these data via sensors, self-diagnoses, disease management or diagnosis and appropriate processing of the data collected. Since mHealth solutions and devices can collect large quantities of personal information, including personal health information, they can process them as well.The mHONcode is a set of ethical, honesty, transparency, quality, and security standards covering various aspects of health apps, including the disclosure of the qualifications of the authors, the funding sources, references, when the content was created and last updated, what the privacy policy is, and how data is stored and transmitted over the internet. The mHONcode motivates health apps editors to be transparent in the production process and in the way to use user’s data. The commitment of a health information provider to implement or comply with the HON code of conduct for health apps is shown by the displaying of a quality label (logo or HONcode seal) on the website.As the adaptation of an already proven trustworthy code of conduct of health websites (the HONcode), the mHONcode is well placed to provide guidance for the next generation of health information providers—mobile apps in this case.KeywordsAppsmHealthTrustworthinessCertificationQuality

Change is constant in healthcare today: the environment is fast-paced, dynamic, and always exciting. Preparing the future health informatics workforce is critical to ensuring that the health and welfare of our global population are realized, regardless of one’s geographical location. To enable health informaticists of the future to excel, creating a framework that helps to establish, prepare, and maintain a commitment to lifelong learning is a shared responsibility of both the employee and the employer. To do so, we must examine and appreciate the various ways professionals engage in professional development activities. And we must provide tools and resources that nurture our future health informatics professionals to enable and support a transformative digital health environment. This is a strategic imperative worthy of effort today and in the future.KeywordsCareer developmentCareer roadmapContinuing professional developmentCertificationDigital educationInformaticsLifelong learningProfessional development planSelf-directed learningWorkforce development