1. Dependable Healthcare Stuart Anderson Dependability Interdisciplinary Research Collaboration (DIRC) UK

2. Dependability IRC oSix-year project funded by UK EPSRC (approx 120 person-years of additional effort, plus grad students and staff), 2000-2006. oUniversities involved: City, Edinburgh, Lancaster, Newcastle, York oDisciplines: Computer Science, Management Science, Psychology, Sociology, Statistics oWide-ranging industrial studies.

3. Why Healthcare? oSignificance: oAgeing European population oCost of provision growing much faster than inflation oThere are significant skills shortages in many key areas (e.g. radiology in the UK). oEU enlargement will shift median income down in the EU.

4. Why Healthcare? oScaling and organizational embedding: oDevices and surrounding protocols: e.g. imaging and treatment machines (Mammography). oHospitals – acute care, processes in single units (e.g. neonatal acute care), and at hospital level, patient information, Electronic Health Record. oPrimary care: coordination of intra- organizational support for patients oSupported living: assistive technologies

5. Dependability Cases oUnderstanding risk perception of stakeholders – failing feckless people is still a failure (particularly true for information systems – c.f. deliberate self-harm ward) oNumber and complexity of stakeholder interactions. oVery complex use of protocols, technologies, and expert judgment. oBalancing medical benefit against lack of good quality evidence.

6. Access Control o“Classical” issues involving conflicting dependability goals – e.g. treatment data for dangerous mental patients. oExisting paper systems have properties that depend on the “affordances” of the artifacts. oChallenge is to build appropriate hybrid access control systems where procedures are strongly supported by automation.

7. Modelling and Simulation oDevising adequate statistical models of complex human-computer processes – results are very sensitive to modelling decisions. oRelationship between component failures and QoS. oModelling temporal validity of data, lack of omniscience, heterogeneous use of knowledge resources.

8. Ambient Intelligence oHealthcare systems could benefit strongly from AmI infrastructure oPotentially “universal” provision - a good platform for AmI and further dissemination. oCurrent AmI scenarios are very culturally biased and fail to recognise some important risks. oWe have a poor understanding of what a dependable AmI infrastructure is.

9. Grid/e-Science oMove to new sensor technologies in medicine means a huge increase in data volumes – e.g. imaging, modelling, personal genetic information? oRisks associated with this volume of data are poorly understood. oSignificant management issues. oGrid claims support for Virtual Organisations – what would dependable support be for a VO?

10. Policy and Practice oPolicy: oStandardisation is difficult in this area e.g. risk management of medical devices. oMove to more diversified, market-like, health system poses severe regulation/coordination problems. oPractice: oNeed for guidance as new technologies diffuse in healthcare systems. oSpecific need for better understanding of the ethical impact of more pervasive systems.

11. Some Issues oCultural differentiation: risk perception, interpreting failure, managing failures. oManaging systems in very uncontrolled situations e.g. medical devices, assistive technologies. oSystems where component failures are frequent yet service delivery is maintained. oTo what extent can we treat humans as components? (learning, adapting, what’s the interface?) oTension between the social and psychological accounts. oInterdisciplinarity is essential.