Dependable Healthcare

Stuart AndersonDependability

Interdisciplinary Research Collaboration (DIRC)

UK

Dependability IRCo Six-year project funded by UK EPSRC

(approx 120 person-years of additional effort, plus grad students and staff), 2000-2006.

o Universities involved: City, Edinburgh, Lancaster, Newcastle, York

o Disciplines: Computer Science, Management Science, Psychology, Sociology, Statistics

o Wide-ranging industrial studies.

Why Healthcare?o Significance:

o Ageing European populationo Cost of provision growing much faster

than inflationo There are significant skills shortages in

many key areas (e.g. radiology in the UK).

o EU enlargement will shift median income down in the EU.

Why Healthcare?o Scaling and organizational embedding:

o Devices and surrounding protocols: e.g. imaging and treatment machines (Mammography).

o Hospitals – acute care, processes in single units (e.g. neonatal acute care), and at hospital level, patient information, Electronic Health Record.

o Primary care: coordination of intra-organizational support for patients

o Supported living: assistive technologies

Dependability Caseso Understanding risk perception of stakeholders

– failing feckless people is still a failure (particularly true for information systems – c.f. deliberate self-harm ward)

o Number and complexity of stakeholder interactions.

o Very complex use of protocols, technologies, and expert judgment.

o Balancing medical benefit against lack of good quality evidence.

Access Controlo “Classical” issues involving conflicting

dependability goals – e.g. treatment data for dangerous mental patients.

o Existing paper systems have properties that depend on the “affordances” of the artifacts.

o Challenge is to build appropriate hybrid access control systems where procedures are strongly supported by automation.

Modelling and Simulationo Devising adequate statistical models of

complex human-computer processes – results are very sensitive to modelling decisions.

o Relationship between component failures and QoS.

o Modelling temporal validity of data, lack of omniscience, heterogeneous use of knowledge resources.

Ambient Intelligenceo Healthcare systems could benefit

strongly from AmI infrastructureo Potentially “universal” provision - a

good platform for AmI and further dissemination.

o Current AmI scenarios are very culturally biased and fail to recognise some important risks.

o We have a poor understanding of what a dependable AmI infrastructure is.

Grid/e-Scienceo Move to new sensor technologies in

medicine means a huge increase in data volumes – e.g. imaging, modelling, personal genetic information?

o Risks associated with this volume of data are poorly understood.

o Significant management issues.o Grid claims support for Virtual

Organisations – what would dependable support be for a VO?

Policy and Practiceo Policy:

o Standardisation is difficult in this area e.g. risk management of medical devices.

o Move to more diversified, market-like, health system poses severe regulation/coordination problems.

o Practice:o Need for guidance as new technologies

diffuse in healthcare systems.o Specific need for better understanding of the

ethical impact of more pervasive systems.

Some Issueso Cultural differentiation: risk perception,

interpreting failure, managing failures.o Managing systems in very uncontrolled situations

e.g. medical devices, assistive technologies.o Systems where component failures are frequent

yet service delivery is maintained.o To what extent can we treat humans as

components? (learning, adapting, what’s the interface?)

o Tension between the social and psychological accounts.

o Interdisciplinarity is essential.