Why Science? | Digital Medicine Translational Research Programme

A project begins with a care problem and decision owner. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 1 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Write the user, setting, decision and alternative. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

Novelty does not establish importance. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Start with a clinical need”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Create a one-sentence need statement. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 1 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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People, information, tools and timing shape use. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 2 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Observe handoffs, constraints and recovery. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

A neat map can miss workarounds. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Map the care workflow”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Draw a fictional workflow. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 2 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Patients, clinicians and operations teams have different goals. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 3 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Use participatory research and accessibility checks. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

A proxy may not represent lived experience. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Define the intended user”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Write three user stories. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 3 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Clinical data acquire meaning through collection, coding and time. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 4 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Record source, consent, definitions and transformations. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

A large dataset can still be biased. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Keep data provenance”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Build a data receipt. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 4 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Labels may come from tests, experts, records or outcomes. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 5 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Measure agreement and adjudicate ambiguity. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

A code is not always a gold standard. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Create trustworthy labels”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Compare three label sources. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 5 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Models learn patterns that need evaluation beyond development data. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 6 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Separate training, validation and external testing. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

Internal performance may exaggerate usefulness. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Train and test algorithms”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Design a split without leakage. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 6 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Adaptive approaches seek useful choices for an individual over time. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 7 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Predefine outcomes, constraints and update rules. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

Personalised output is not guaranteed optimal. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Personalise optimisation”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Simulate an adaptive table. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 7 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Sensors convert physical phenomena into repeated measures. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 8 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Test calibration, placement, drift and adherence. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

Frequent data are not automatically accurate. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Evaluate wearables and sensors”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Plan a reliability check. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 8 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

Contents · Previous chapter · Next chapter

Some systems recommend or deliver actions. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 9 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Add constraints, fail-safes and supervision. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

Automation can amplify error. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Develop interventional technologies”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Draw a safe control loop. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 9 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

Contents · Previous chapter · Next chapter

Trials compare outcomes, workflows and harms. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 10 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Select a comparator and patient-relevant endpoint. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

Engagement alone does not prove benefit. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Design clinical trials”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Draft an outcome set. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 10 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

Contents · Previous chapter · Next chapter

Interfaces influence attention, workload and recovery. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 11 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Use task analysis and simulated use. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

User error may reveal a system problem. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Study human factors”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Audit a paper alert. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 11 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

Contents · Previous chapter · Next chapter

Evidence expectations depend on intended use and risk. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 12 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Align claims with validated functions and current guidance. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

A prototype is not an authorised device. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Navigate regulation”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Match claims to evidence. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 12 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Connected systems need threat modelling and update plans. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 13 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Test foreseeable misuse and component dependencies. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

Encryption alone is not lifecycle security. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Protect cybersecurity”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Build a threat map. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 13 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Health data can be sensitive, linkable and reusable. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 14 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Minimise collection and define access and retention. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

De-identification leaves some risk. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Protect privacy and consent”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Design a minimum-data form. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 14 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Performance can drift as populations and workflows change. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 15 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Track quality, equity, incidents and versions. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

One validation cannot guarantee continuing safety. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Monitor after deployment”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Write three monitoring triggers. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 15 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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Value includes outcomes, resources, access and burden. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 16 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Compare distribution as well as averages. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

Lower cost can coexist with exclusion. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Test value and equity”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Build a value scorecard. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 16 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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The receipt links need, data, model, interface, trial and monitoring. Digital medicine joins a clinical question to data, algorithms, devices, interfaces and implementation. WisDM describes in-house technologies and partnerships aimed at practice-changing medicine. Chapter 17 therefore treats a digital system as a complete care pathway with users, inputs, outputs, consequences and fallback routes, rather than a clever model demonstrated in isolation.

Archive versions, approvals, incidents and revisions. Good evidence begins with version control. Researchers record the intended population, data provenance, label process, device or software version, comparator, endpoint, missingness, security controls and monitoring plan. They separate development from evaluation and study whether workflow, usability and human review change what the system achieves in practice.

Documentation does not make software infallible. A high performance score does not establish clinical usefulness. Future patients may differ from training data, a sensor may drift, an alert may arrive too late, and an interface may encourage over-reliance. Digital medicine therefore needs clinical validation, appropriate regulatory review, cybersecurity, privacy, human-factors testing and continuing surveillance; an educational article cannot replace professional care.

For “Keep a digital medicine receipt”, design a paper prototype using imaginary information. Name the user, the decision, the minimum data, the safe fallback and the evidence that would justify release. Assemble a release receipt. Do not use personal health records or test a medical claim on classmates. The point is responsible system design, not diagnosis.

Did you know? The same software can behave differently after deployment because disease prevalence, staffing, connectivity, equipment and attention have changed. Chapter 17 turns that surprise into a scientific question: which part of the sociotechnical system moved, how would the team detect it, and who owns the response before a small drift becomes harm?

Related pathways include computing, statistics, biomedical engineering, medicine, nursing, design, psychology, health economics, regulation and operations. Current programmes and roles have requirements that must be checked with their official owners. A valuable starting habit is connecting every output to its evidence source, decision owner, uncertainty and revision history.

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