The impact of information technology on the quality and by va12012

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									The impact of information technology on the
quality and safety of health care
Systematic literature overview and synthesis




 Professor Azeem Majeed
 Head of Department & Professor of Primary Care
 Department of Primary Care & Social Medicine
 Imperial College London
Why e-health?

• Increasing burden of care (e.g. increased life
  expectancy, long term conditions, range of treatment
  options)
• Rising healthcare costs
• Continuing health systems inefficiencies
• Variations in quality of care
• High prevalence of medical errors
• Greater public scrutiny of government spending
• Patients and the public want a greater say in decisions
  about their health & healthcare
The emergence of e-health

• Rapid IT developments across all sectors, but diffusion
  still relatively limited in health care sector
• Need to capitalise on potential of IT to increase
  efficiency, effectiveness and safety
• But implementation of IT also has potential to introduce
  new risks & errors associated with technology or users
• New IT solutions often rapidly introduced, on a large
  scale, despite limited evidence of effectiveness or
  safety
• Many IT innovations remain un(der)studied and
  evidence of their effectiveness is inconsistent
Scope of the review
•   Commissioning brief emphasised areas of high
    relevance for CfH, namely, hence areas targeted in this
    review:

•   Storage and retrieval of medical records and data
    »   Health information exchange and interoperability
    »   Electronic health records
    »   Computer history taking systems
•   Supporting professional decision making
    »   Computerised decision support systems
    »   ePrescribing
•   Socio-technical considerations for eHealth development
    and deployment
    »   Human factors
    »   Effective implementation and adoption of eHealth applications
Conceptual work

Purpose:
Develop theoretical grounding by profiling the concepts
of eHealth, quality and safety.

Analytic framework draws on:
Core applications of eHealth, represented within the
conceptual model developed by Pagliari et al.
Taxonomy of patient safety risks developed by
WHO/JCAHO Key elements of healthcare quality
frameworks
Headline deliverables of NHS Connecting for Health and
other key elements of the programme
Mapping healthcare quality
Multiple conceptual approaches (e.g. Campbell,
Donabedian, Shekelle, Schuster)

Key attributes of quality
• Effectiveness (outcomes of care,
  appropriateness/evidence-basis of care given)
• Efficiency (streamlining care processes, reducing costs)
• Acceptability (satisfaction, patient-involvement)
• Equity (reducing variations in practice)
• Accessibility (speed and ease of obtaining care or
  advice)
  Quality map
                                                        Efficiency
                                                   Streaming processes
                                                Increasing access (to data)
                                                      Reducing costs




               Acceptability                           Effectiveness                     Equity
         Enhanced patient satisfaction   Clinical appropriateness (evidence-base +   Enhanced access
        communication, empowerment                       tailoring)                  Reduced variation
                                                      Clinical impact




                                                           Access
                                            To services, knowledge, data, people,
                                                           support




(Derived from multiple sources, e.g. Campbell, Donabedian, Shekelle, Schuster)
            Annotated JCAHO/WHO Patient Safety Taxonomy (a)


                                                                TYPE OF PROBLEM


                           Communication                Patient Management              EHR                        Clinical Performance
                                                         (Organisation level)                                       (Practitioner level)

                                    EHR/Coding                                             e-Comms
  Information quality
                                    Patient websites     Tracking or follow-up             e.g. lab,      Pre-Intervention               Intervention
                                                                                           booking,     Questionable diagnosis    Incorrect procedure enacted
Advice or Interpretation                                                                   referral,                               Correct procedure omitted
                                    Remote advice
                                                        Referral or consultation           EHR
                                    for professional;
                                                                                                          Post-intervention
   Consent Process                  remote patient
                                                                                                         Inaccurate prognosis
                                    consultation
                                                                                   e.g. e-mail or web
    Documentation                                                                  consultation
                                                                                                                                 CDSS
                                     EHR/ e-forms
            (b)

                                                                                                         CAUSE


                                                    SYSTEMS                                                                                   HUMAN
                                                                                                                                               (Error)
              Organisational                                                    Technical


                               Management                                                                           Patient              Practitioner                   External factors
IT planning &             e.g. staffing/training,              Facilities
procurement                  safety financing            e.g. design, malfunction
                                                               obsolescence                                                                              Skills based            Data entry,
                                                                                                                                                         (execution)             software use
                                Culture
Towards change           e.g. around information                                                                  Patient factors
& innovation            sharing or event reporting                                                                                                       Rule-based
                                                         IT usability,                                                                                                           Data
                                                         accessibility,                                                                                   (retrieval)
                                                                                                                                                                                 extraction
e-protocols (e.g.        Protocols/Processes                                                Negligence
                           e.g. documentation,           dependability,                                               IT skills, ability to
referral), coding &                                                                        Recklessness                                             Knowledge-based
                          incentives, standards          + QI loops                 Intentional rule violations       recognise good &
QoF, IT standards                                                                                                                                    (interpretation)
                                                                                                                      poor quality info.
                        Knowledge Transfer                                                                            Use of unsecure e-
IT skills. Process           e.g. training &                           Lack of tools/procedures for                   comms for sensitive
redesign                supervision around safety                      tracking, auditing, alerting (e.g.             info.                                     Data interpretation
                                                                       Shipman)                                                                                 (e.g. images, reports,
                                                                                                                                                                records) information,
                                                                                                                                                                evidence, decision
                                                                                                                                                                support
    (c]

                                                           IMPACT



                              Medical
                                                                                Non-Medical
                              (Harm)




              Psychological                 Physical              Legal            Social         Economic




Due to faulty infor-                    Morbidity and        Confidentiality   Poor adoption   Cost to modify or
mation, interventions or                mortality due to     & negligence      of technology   replace IT & re-
confidentiality breaches                poor CDSS, e-        suits                             train staff
                                        comms or EHR                                           Legal settlements.
                                        integrity                                              Lost bed days &
                                                                                               waiting times
Challenges to synthesising the evidence

•   Large and rapidly expanding literature, poorly
    indexed, of variable quality

•   Hence we used the following approach:
    » Clarifying definitions, description and scope for
      deployment to reflect on potential risks & benefits
    » Identifying empirically demonstrated benefits and
      risks, using exemplar subject areas and/or
      detailed case studies with relevance to CfH
    » Highlighting clinical, policy and research
      implications
    Example: Electronic Health Records

    Range from simple digital databases to complex systems
    integrating electronic communications (e.g. ePrescribing) and
    active knowledge support (e.g. clinical decision support).
                          Theoretical Benefits
• Elimination of pen and paper               • Supporting clinical governance activities
• Minimised storage space                    • Facilitates electronic record transfer
• Increased legibility                       • Supports electronic clinical
• “Searchability”                              communications e.g. e-Prescribing,
• 24/7 access                                  results viewing
• Efficient longitudinal record keeping      • Enables automatic alerting and data
                                               collection
• Supports integrated care
                                             • High level of data security, privacy and
• Instant health information exchange          confidentiality
• Interactive recording of information       • Facilitates research
• Encourages use of controlled               • Increased satisfaction
  terminology
                                             • Increased productivity
• Potential to integrate patient data with
  evidence and CDSS                          • Economy of scale savings
Electronic Health Records ctd


                        Theoretical Risks

• Vulnerability to power cuts and system failures
• Increased time for some data entry
• Provider resistance to change
• Lack of interoperability
• Security/privacy risks
• Poor data quality (coding)
• Reliance on possibly incomplete patient data
•Increased resource utilisation at early stages
Electronic Health Records ctd
                     Empirical evidence of benefits

Good evidence of
    • Improved legibility
    • time saving for some professionals (nurses)
    • facilitation of audit, performance management & secondary analysis of
      routine data
Moderate evidence of
    • improvements in clinical decision         making   for   preventive   care
      (complicated by integration of CDSS)
Little evidence of impacts on
    • clinical outcomes
    • Cost (some efficiency gains in ePrescribing)
    • Patient safety
Empirical evidence of risks:
•       Increased doctor time for data entry and retreival
Example: Supporting professional decision making




       Clinical decision support
                  and
       e-supported prescribing
Example: Electronic Prescribing


                      Theoretical benefits

•   Increased legibility
•   Standardisation of prescribing
•   Patient specific support for prescribing
•   Reduced lost orders
•   Instantaneous transmission to pharmacy
•   Instant reporting that item is out of stock
•   Alerts for unfilled and/or non-renewed prescriptions
•   Improved documentation
•   Tailored prescriber feedback
•   Facilitated clinical governance activities
•   Cost-savings
Electronic Prescribing

                    Empirical evidence of benefit

● Obliteration of illegibility

● Reduction of prescribing errors in inpatients
● Increased time for patient care (pharmacists)
● Reduction of turn-around times
● Determining more appropriate drug dosage
● Increased corollary orders

● Reduction of preventable adverse drug events (ADEs) in inpatients
● Reduction of prescribing errors in outpatients
● Guideline adherence by outpatient providers
● Cost-benefit ratio
Electronic Prescribing

                     Empirical evidence of risk

● Poor specificity of alerts (excessive alerts)
● Poor sensitivity of alerts (missed alerts)
● Alert fatigue (non-adherence)

● Increased work load
● Poor system fit
● Introduction of errors (human and machine)
Overarching observations I

• Ripe environment for heathcare IT
  • Potential to ameliorate burden of care caused by changing
    health demographics, proliferation of treatments & costs
  • Rising public expectations for service quality and efficiency
  • Rapid advances in IT
  • European lead in eHealth industry
• Gap between theoretical & demonstrated benefits
  • While seminal reports recognise the potential impact of
    eHealth on quality and safety, numerous human and technical
    barriers need to be overcome before these systems are
    embedded sufficiently for this potential to be realised. Most
    eHealth technologies are supported only with face validity or
    weak empirical evidence. There is a need for more evaluation
    across the lifespan of technology design and implementation.
Overarching observations II
• Variable quality of evidence limits interpretation
  • Relatively few eHealth trials have evaluated safety outcomes
  • Lack of primary research assessing quality outcomes
    (implicit assumption that benefits are obvious)
  • Studies demonstrating greatest benefits often come from
    academic medical centres and concern „home grown‟
    systems, so results may not be generalisable to less
    conducive environments. Joint roles as
    developer/evaluator/user may introduce bias.
  • Inconsistent use of outcome measures make it hard to
    generalise across studies
  • Poorly theorised interventions and studies (failure to
    anticipate complexity)
  • Poor outcome definition and measurement
  • Overestimating likely effect sizes.
  • Inappropriately short evaluation timescales (not enough time
    for interventions to bed-in and improvements demonstrated)
Overarching observations III

•   Vast and expanding body of literature is poorly indexed, appraised and
    ordered
    • Large body of work at intersection between eHealth, quality and safety
    • Overlap between different eHealth applications & variation in contexts of
      implementation make it difficult to produce meaningful taxonomic
      frameworks and assess likely effectiveness & generalisability

•   Inadequate attention to human and socio-cultural factors
    • Human factors (e.g. poor usability) may compromise the fitness-for-purpose
      of otherwise sound applications e.g. evidence that many doctors routinely
      ignore e-alerts where their value is not recognised. Results obtained in
      controlled trials may therefore not generalise to real world settings. Greater
      attention to design considerations, such as tailoring of support and
      preventing over-rides for critical alerts could improve this situation.
    • Contextual factors (local culture, rewards/incentives and previous technical
      history) can strongly influence the transfer potential of technologies and
      studies should document these „softer‟ influences to aid interpretation of
      results and inform future implementations.
Conclusions
• Greater attention to sociotechnical factors in the design,
  implementation and evaluation of eHealth interventions
  is necessary to maximise their potential

• Demonstrating their potential value for improving
  quality, safety and efficiency is dependent on the
  application and transparent reporting of
  methodologically sound and theoretically robust
  evaluation research

• Such evidence is of high value to suppliers and
  policymakers (e.g. for business case demonstration) as
  well as health service providers and patients.
Some key issues for research
• Giving patients online access to their medical records. What are
  the confidentiality, technical, ethical and legal issues? How will
  it impact on the quality of care and accuracy of electronic
  medical records?

• Giving the public the information they need to make informed
  choices about their health and their healthcare. Use of web
  technologies, social networking etc.

• Self management programmes and patient empowerment for
  people with long term illnesses.

• Use of information technology to improve quality of care, for
  example through the use of computerised decision support
  systems.

• Developing capacity to process very large volumes of data for
  public health surveillance quickly and efficiently to allow early
  detection of threats (e.g. flu outbreaks, adverse drug reactions).
Acknowledgements



Thanks to the members of the team:

•   Dr Josip Car, Imperial College
•   Ms Ashly Black, Imperial College
•   Professor Aziz Sheikh, Edinburgh
•   Dr Claudia Pagliari, Edinburgh

								
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