Module 4 – Data-driven and technological clinical practice
In this module, you will explore how technology and data support clinical practice in Hospital at Home. The module introduces technology as a prerequisite for providing hospital-level care outside the hospital and considers how medical technology can be safely used in the patient’s home. You will examine how data is collected, interpreted, and applied in everyday clinical decision-making, while also reflecting on the limitations and uncertainties that may arise. The module also highlights the importance of reflective support and digital health literacy, both for healthcare professionals and for patients and family members involved in care.
Technology as a prerequisite in Hospital at Home
This chapter introduces technology as a clinical prerequisite for Hospital at Home. It highlights how digital monitoring, communication tools, and data exchange enable care in the home, while also changing the conditions for clinical decision-making.
Technology as a clinical prerequisite
Technology is a fundamental prerequisite for HaH to function. Digital monitoring, communication solutions, and structured data exchange enable advanced care to be delivered in the home, while simultaneously transforming how clinical work is conducted.
In traditional hospital care, observation, data collection, and decision-making occur in a controlled environment with direct access to the patient. Technology is standardized, well established, and largely invisible in daily practice. Clinicians are expected to rely on equipment functioning correctly within established routines.
In HaH, this stability shifts. The clinical basis for decision-making becomes more indirect, distributed, and dependent on a combination of technology, patient participation, and digital communication. Technology must therefore not only function, it must be understood and critically appraised in each individual situation.
Changing conditions
When care moves into the patient’s home, both responsibility and context change. A greater proportion of device handling falls to patients and their relatives, making their digital literacy directly relevant to the quality and safety of care.
At the same time, technical and infrastructural conditions vary more than in hospital settings. Connectivity, power supply, physical placement of equipment, and technical stability can all affect both data quality and functionality. As a clinician, you must actively evaluate these factors and understand how they influence the reliability of the clinical data.
Compared with traditional care, you need to assess whether the patient and relatives can use the equipment correctly; whether the home environment is suitable for care and technology use; whether the technical infrastructure is sufficient; whether there are functioning contingency plans for technical failures or anomalous data; and whether the available information is adequate for safe clinical decision-making.
Technological understanding in clinical practice
In HaH, many clinical decisions are made remotely, whether contact occurs through in-person home visits, video consultations, or data from medical devices. This means that technology is not merely a support for care, but an active component of the clinical decision-making process. The ability to assess a patient’s condition is therefore closely intertwined with how you understand, interpret, and evaluate the technology being used.
Technological understanding in HaH does not require mastery of all technical details, but rather the ability to understand the function, limitations, and clinical implications of the technology. The key is determining whether the technology is reliable and fit for purpose in the given context.
Technology thus becomes a variable in clinical reasoning rather than a neutral source of information. You need to identify when it provides relevant and reliable information, when it risks limiting or biasing your assessment, and when it introduces uncertainty into the decision-making process.
Stina
Stina’s physician reviews the information coming in from the home. Stina is being treated in HaH for suspected pyelonephritis and worsening heart failure symptoms, and she has low digital literacy, reduced mobility, and depends on staff for monitoring.
The physician considers not only the reported values, such as oxygen saturation, respiratory rate, temperature, and weight, but also how the measurements were taken. Was the pulse oximeter positioned correctly? Was Stina sitting or lying down? Has her weight been measured on the same scale and under similar conditions? If the data suggest improvement, but the nurse reports that Stina is more breathless during conversation, the physician cannot treat the technology as a neutral answer.
In this situation, technology supports clinical assessment, but it does not replace it. The physician must decide whether the information is reliable enough to continue treatment at home, whether a home visit is needed, or whether Stina should be assessed in hospital. For Stina, the technology becomes one part of the clinical picture, alongside symptoms, functional ability, home support, and professional judgement.
Medical technology in the home
This chapter introduces medical devices and CE marking in Hospital at Home. It highlights the difference between medical devices and consumer products, and why intended purpose, setting, user competence, servicing, and quality control matter when care is delivered in the home. The chapter explores how healthcare professionals assess whether a device is appropriate, safely used, and reliable for the specific patient, clinical purpose, and home context.
Medical devices and CE-marking
Medical devices are defined by their medical purpose – for example to diagnose, treat, or monitor a condition. In HaH, this includes everything from monitoring equipment to digital platforms and decision support systems. Medical devices are subject to stringent EU regulations and must undergo thorough testing and risk assessment before being marketed.
Consumer products by contrast, are intended for general everyday use without a medical purpose. These are regulated primarily through general product safety legislation to ensure they are not hazardous during normal use. Consumer products may include items in the patient’s home such as thermometers, smart watches, etc.
The key difference lies in the product’s intended purpose and associated risk level, with medical devices subject to significantly higher requirements for safety and documentation. Medical devices often have a narrowly defined intended use, and a device approved for hospital use may not necessarily be approved for use in the home.
Medical devices used in hospitals (such as ECG machines, pulse oximeters, and even thermometers) are subject to regular servicing, calibration, and quality control. Established procedures are in place for incident reporting, and dedicated teams often oversee ongoing quality assurance and compliance. This structured framework is important to consider when relying on a patient’s own devices, such as a thermometer or pulse oximeter, particularly in a hospital-at-home setting, where the same level of control and standardization may not be guaranteed.
Below is an overview of medical devices that may be used in HaH.
An overview of medical devices that may be used in HaH.
Intended purpose and off-label use
Consider a wearable device with a built-in single-lead ECG feature that is CE-marked as a medical device. Its intended purpose, as stated by the manufacturer, is narrow:
Record a single-channel ECG (similar to a Lead I) in adults aged 22 years or older.
Classify the recorded rhythm as either atrial fibrillation or sinus rhythm on a classifiable waveform.
Intended for users without a pacemaker, ICD, or other implanted electronic cardiac device.
Provide informational output only, to supplement clinical assessment rather than replace it.
The manufacturer explicitly states that the feature is not intended for users under 22, not validated for users with other known arrhythmias, and not intended for users with pacemakers or ICDs.
If the device is used outside this narrow scope, for example to monitor rhythm in a patient with a pacemaker, or to diagnose ventricular arrythmias, the use falls outside the CE-marked intended purpose. This is effectively off-label use. The manufacturer has not validated performance in these situations, has not submitted evidence to the notified body for these indications, and assumes no regulatory responsibility for the output. Clinical responsibility shifts to the healthcare professional or institution deploying the device.
This illustrates why, in HaH, it is important to check that a device's CE mark covers the specific population, setting, and clinical purpose in which it will be used.
CE Marking
CE marking indicates that a product meets EU requirements for safety and performance for its intended use and that the manufacturer is responsible for its design and post-market surveillance. However, it is crucial to understand what CE marking does not imply. It does not guarantee optimal performance in every clinical situation, nor does it account for variations in the home environment, user competence, or local workflows.
Data in clinical practice
This chapter introduces data and communication as central components of clinical decision-making in Hospital at Home. It highlights how health data collected in the home must be interpreted in relation to context, data quality, patient symptoms, and clinical judgement.
Data and communication
In HaH, many clinical decisions are made remotely. Contact may occur through video consultations, telephone, in-person home visits, or data from medical devices. This means that information often must be interpreted and communicated without the clinician having direct access to the patient.
Clear communication therefore becomes a central component of the clinical decision-making process and part of the treatment itself.
In HaH healthcare services are delivered in the patient’s home. This means that interactions between healthcare professionals and patients may take place both physically in the home and digitally through technology. In both situations, it is not only professional competence, but also the ability to communicate clearly, effectively, and empathetically, that determines the quality of care and treatment.
Communication involves more than words. It is about establishing connection, achieving mutual understanding, and ensuring that information is both received and understood. In digital consultations, this occurs under different conditions than during face-to-face encounters—but the fundamental principles of effective communication remain the same.
Data as a basis for decision-making and visualization
Health data is a key component of clinical decision-making within HaH, but its quality and context may vary more than in traditional care. It requires new skills: you need not only to read data, but also to interpret it within the appropriate context. Data is generated in the patient’s home but interpreted in a different clinical setting, meaning that understanding how it was produced must always be part of the assessment.
When data change context
In HaH, much of the data is collected in the patient’s home - but they are often assessed and used elsewhere, such as in a hospital setting or during a digital team meeting. This raises important questions:
Where and how were the data collected?
What do they show—and what do they not show?
What might you risk overlooking?
Context is crucial. An oxygen saturation reading taken with a pulse oximeter, used by the patient, in a cold kitchen must be interpreted differently from the same measurement taken on a monitored hospital ward.
Data analysis in HaH is complex
In HaH, data are generated from multiple technologies and systems, for example:
Home-based monitoring equipment for vital signs such as blood pressure, pulse, weight, body temperature etc.
Digital diaries and applications
Electronic health records
This means you may encounter:
Alerts that do not align with the overall clinical picture
Delayed or incomplete data
Measurements of uncertain quality
Your role is not only to read data—but to interpret and apply them as part of the overall clinical assessment.
Digital competencies
For the HaH model to function effectively, healthcare professionals require both strong clinical expertise and a high level of digital competence. Digital technology is a cornerstone of the model, but its effectiveness depends on users being able and willing to engage with it. Digital competence therefore needs to be developed and maintained over time.
The most important competencies required as a healthcare worker:
Basic information technology literacy
Health information management
Digital communication
Ethical, legal, and regulatory requirements
Data privacy and security
Healthcare workers in hospital at home programs require specific digital competencies including:
Proficiency with electronic medical records
Remote patient monitoring technologies
Digital communication platforms
Telehealth systems
Promoting digital competence requires:
Education that combines theoretical knowledge with practical training
Opportunities to practise in safe and supportive environments
Ongoing support and mentoring during implementation
Continuous updating of knowledge and exchange of experience
In this video, you will be introduced to how digital competence can be strengthened in practice: through education, hands-on training, support, and continuous learning.
Why does HaH require specific data competencies?
The systems used in HaH often come from different providers and vary in:
Terminology and definitions
Interfaces and system logic
Data visualisation and alert thresholds
This requires both digital skills and critical reflection:
Are the data reliable?
Do you understand what they show—and what they do not show?
What information is missing for you to make a safe clinical decision?
Digital data visualization, where measurements are presented as trends over time, can facilitate the identification of gradual changes that might otherwise be missed. At the same time, these visualizations require critical interpretation, as irregular measurements, missing data points, and variable handling can create patterns that do not reflect clinical reality.
Clinical work therefore involves continuously balancing individual values against trends, data volume against data quality, and visualized information against the patient’s symptoms.
Stina
Stina receives HaH care for heart failure. Her weight, blood pressure, and oxygen saturation are recorded digitally and displayed as trends in the HaH platform.
One morning, the nurse sees that Stina’s weight has gradually increased over several days. At the same time, there are missing measurements, and one oxygen saturation value appears unusually low. Before making a clinical decision, the nurse needs to consider whether the data are reliable: Were the measurements taken correctly? Are the missing values important? Does the trend match Stina’s symptoms?
When the nurse contacts Stina, she reports increased tiredness and swollen legs. The data alone were not enough, but together with Stina’s symptoms they supported the need for clinical action.
This shows why clinicians in HaH need specific data competencies. They must be able to interpret trends, recognise missing or uncertain data, and combine digital information with clinical assessment to make safe decisions.
Clinical decision-making
This chapter introduces clinical judgement and integrated assessment in Hospital at Home. It highlights how healthcare professionals combine data, symptoms, patient context, baseline status, and risk when decisions are made at a distance.
Clinical judgment and integrated assessment
Clinical judgment becomes even more critical when the decision-making basis is indirect. You must integrate data, symptoms, context, and risk into a coherent assessment.
This involves continuously identifying what is known, what is missing, and the risks associated with watchful waiting. Structured tools such as NEWS2 can provide support but must always be interpreted in relation to the patient’s baseline and the current clinical context.
In HaH, there is no healthcare professional “just around the corner”—which means that both technical and human errors can have greater consequences.
Clinical sight and systematic observation
Specific challenges in the home
Limited understanding of instructions → Provide clear support materials and contact information
Incorrect use of equipment → Provide hands-on training before discharge to home care and plan follow-up to reinforce learning and address any remaining difficulties
Technical issues (e.g. poor connectivity) → Verify functionality before discharge and define a backup plan, including alternative communication channels and clear instructions for the patient
App errors or access issues → Use checklists and ensure access to support
Known risks in a new context:
Faulty or depleted equipment → Provide backup devices
System failures → Be familiar with contingency procedures
Use of non-approved equipment → Explain the importance of standardised solutions
Many of these issues are familiar from hospital settings—but in HaH, it is essential to anticipate and prevent them. This becomes especially important when you are not physically present.
Reflective support
This chapter introduces the TECS model as a reflective support for understanding technology in Hospital at Home. It highlights how technology, engagement, complexity, and shift can be used to analyse digital tools, monitoring, communication, and decision support in clinical practice.
The TECS Model as clinical reflective support
The TECS model provides a simple framework for analyzing the use of technology in clinical practice. It can be applied to monitoring, digital communication, and decision support, helping to clarify how technology emerges in the interaction between technology, users, and organization.
The model is based on four perspectives:
Technology - concerns what the technology enables and what limitations affect outcomes.
Engagement - describes how the technology is used in practice—whether actively and contextually or more routinely.
Complexity - highlights how workflows, responsibilities, and collaboration influence its use.
Shift - focuses on how technology changes work practices, workflows and clinical decision-making over time.
In HaH, the use of the model could be particularly relevant because technology is no longer a background support but a direct component of the decision-making process.
The TECS model, Gars Jensen (2016)
Clinical relevance
TECS can be used as a rapid cognitive aid when introducing new technology, managing uncertain data, handling deviations, or developing workflows. The model highlights that technology use is always context-dependent and that the same system may function differently depending on the situation.
A central principle in HaH is that technology is not neutral. Its use affects patient safety, workload, and quality of care, and therefore requires active clinical appraisal in every case.
Digital health literacy
This chapter introduces the concept of digital health literacy and presents two key models: the Lily Model and the eHealth Literacy Framework (eHLF). It provides insight into how digital skills, understanding, and motivation influence collaboration in HaH, as well as how you, as a healthcare professional, can identify and support patients’ competencies.
Why is digital health literacy important in Hospital at Home?
Digital solutions are becoming increasingly prominent in healthcare—serving as tools, platforms for collaboration, and integral components of treatment itself. In Hospital at Home, this becomes particularly evident, as technology is brought into the patient’s home. This places demands on both patients’ and healthcare professionals’ digital health literacy.
In HaH, care is delivered in the patient’s home, where technology plays a central role. Patients and their families are often required to record measurements, navigate applications, interpret data, and communicate digitally with healthcare professionals. Your knowledge of health technologies, and your ability to support the patient’s understanding, are therefore crucial.
When referring to technology in HaH, this includes both physical devices—such as tablets, monitoring equipment, and infusion pumps—and the systems and platforms that enable data entry, transmission, and communication.
Digital health literacy encompasses the ability to access, understand, and use digital solutions that support treatment, care, and collaboration. This includes technical skills, critical appraisal, and understanding of health information, as well as trust in and motivation to use technology.
By understanding these competencies—in yourself, your colleagues, and your patients—you can strengthen collaboration, reduce the risk of misunderstandings, and contribute to safety and quality throughout the care pathway.
In this chapter, we will explore two models that can be used to understand and assess digital health literacy—both your own and that of the patient:
The Lily Model – comprising six core types of skills
The eHealth Literacy Framework (eHLF) – comprising seven dimensions that take both the individual and the context into account
Hamid
Hamid receives HaH care for worsening heart failure. Each morning, he is asked to measure his weight, blood pressure, and oxygen saturation using equipment provided by the HaH team. His daughter helps him enter the values into a digital platform.
One morning, Hamid forgets to weigh himself and enters yesterday’s value instead. The nurse notices that the data do not match his symptoms: he reports increased breathlessness and swollen legs. During the video call, it becomes clear that Hamid is unsure why daily weight matters and when he should contact the team.
This shows why digital health literacy is relevant for clinicians. The nurse needs to assess not only Hamid’s clinical condition, but also whether he understands the technology, the data, and their meaning. By adapting the instructions and involving his daughter, the nurse can reduce misunderstandings, improve monitoring, and support safer care at home.
The Lily Model, Norman & Skinner (2006)
The Lily Model
The model describes six types of skills that are necessary for navigating health technologies and digital solutions.
These skills are collectively referred to as the Lily Model, as they are illustrated as petals of a lily. The model highlights that a patient’s ability to use health technology is composed of multiple competencies—and that these interact with one another.
The six skills are:
Traditional literacy and numeracy – Can the patient read and understand text and numbers?
Media literacy – Can the patient understand and critically evaluate health information in, for example, videos or on social media?
Information literacy – Can the patient search for, find, and assess information?
Computer literacy – Can the patient use applications, patient portals, and digital devices?
Science literacy – Does the patient have a basic understanding of health and scientific concepts?
Health literacy – Can the patient understand health-related information and instructions—and act upon them?
These skills are not only relevant for patients—they also apply to you as a healthcare professional. By being attentive to them, you can better support patients in using technology and more easily identify potential barriers.
Alex
Alex, being treated in HaH for a Crohn’s flare, has high digital capacity and is comfortable using digital tools in his Hospital at Home care. Looking at his situation through the six skills in the Lily Model shows that digital health literacy is more than technical ability.
Alex can read written instructions and understand numbers such as temperature, symptom scores, and fluid intake. He is used to digital media and can search for information about Crohn’s disease, but he may still need guidance in judging which sources are reliable. His computer literacy is strong, as he uses a smartwatch, symptom-tracking app, and video consultations.
At the same time, the HaH team needs to assess whether Alex understands the clinical meaning of the information he receives. For example, does he know which symptoms suggest deterioration, when blood in the stool is concerning, or when reduced fluid intake should prompt contact with the team?
The eHealth Literacy Framework, Ole Nørgaard et al. (2015)
The eHealth Literacy Framework (eHLF)
While the Lily Model focuses on the individual skills a person needs to use health technology, the eHLF offers a broader perspective.
The eHLF consists of seven dimensions that together describe the factors influencing a person’s ability to understand and use digital health solutions. It is not only about what the patient is capable of—but also about how systems are designed and the level of support provided.
The seven dimensions:
Ability to find, understand, and use digital health information – Can the patient navigate systems and understand what is required?
Ability to actively engage with digitally supported healthcare – Does the patient have the motivation and trust to participate digitally?
Ability to take responsibility for one’s own health using technology – Does the patient feel empowered to act, or left to manage alone?
Access to appropriate digital services – Are the technologies user-friendly and accessible to the patient?
Suitability to the individual’s needs and context – Are factors such as life situation, language, and physical ability taken into account?
Experience of support from healthcare professionals when using technology – Does the patient feel guided and supported?
Perception that the technology is designed to support health – Is the solution meaningful and useful to the patient, or does it feel unfamiliar or irrelevant?
The model highlights that digital health literacy is not solely dependent on the patient’s individual abilities, but also on system design and the support provided by healthcare professionals.
Hamid
Hamid receives HaH care for chronic heart failure, increasing leg swelling, and reduced mobility. He lives alone, has limited informal support, and relies on staff contact rather than managing digital systems independently.
Using the eHealth Literacy Framework, Hamid’s situation shows that digital health literacy is not only about his own abilities. He may have difficulty finding and using digital health information by himself, and he may not feel confident taking responsibility for monitoring his condition through technology. For example, he may need support to understand what changes in weight, swelling, or breathlessness mean, and when to contact the HaH team.
The suitability of the digital solution is therefore important. A complex app or written digital instructions may not be appropriate if Hamid does not use them confidently. Instead, regular phone or video contact, simple reminders, and shared access to the care plan for his children may better match his needs and home situation.
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