Consumer Health Informatics: Using Technology to Enhance Partnerships with Our Patients

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Consumer Health InformaticsUsing Technology to
Enhance Partnerships with Our Patients

• Anna M. McDaniel, DNS RN FAAN
• Indiana University
• November 21, 2006

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Objectives

• Describe current trends and future directions in
  consumer health informatics
• Examine issues surrounding consumers use of
  health information technology
• Discuss examples of consumer health informatics
  research

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Consumer Health Informatics

• Branch of health informatics that
• Analyzes information needs of consumers
• Develops, tests, and implements strategies to
  deliver health information to consumers
• Integrates consumer preferences into health care
  information systems
• Eysenbach, G. (2000). Consumer health
  informatics. British Medical Journal, 320,
  1713-1716

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Consumer Health Informatics

• Use of information technology to support the
  health and communication needs of patients and
  lay persons
• Brennan PF. (1999). Health informatics and
  community health support for patients as
  collaborators in care. Methods of Information in
  Medicine. 38, 274-8,

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Consumer Health Informatics

• Integration of consumer health information and
  information technology in an environment of
  shared healthcare decision-making that supports
  effective self-health action
• Lewis, D. Friedman, C. (2002). Consumer
  health informatics. In M.J. Ball, K.J. Hannah,
  S.K. Newbold, J.V.Douglas (Eds.). Nursing
  informatics Where caring and technology meet
  (3rd ed.). New York Springer-Verlag.

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History of CHI

• Consumer movement of 1970s
• Increased demand for information
• Greater participation in medical decision
  making
• Prominence of self-help phenomenon of 1980s
• Huge increase in health information for lay
  audience
• Widespread use of the Internet
• Increased dramatically throughout 1990s
• In 2005, 78.6 of Americans online

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Pew Internet and American Life

• 113 million American have used web for health
  information (2006)
• Women are more likely to seek health information
  online than men
• Majority (56) seek information for others
• Over half (53) report that online health
  information had an impact on behavior or
  decisions
• www.pewinternet.org

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CHI Continuum
Give/receive support
Obtain education/ information/treatment
Communicate with providers
Access to personal medical information
Provide information
Degree of Consumer Autonomy
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Personal Health Record

• Tool for collecting, tracking, and sharing
  important, up-to-date information about an
  individuals health or the health of someone in
  their care
• Facilitates informed health care decisions
• Allows individual to be active partner in care

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Impact on Patient-Provider Relationship

• Patient role is evolving
• Informed patients more satisfied and better able
  to cope with illness
• Shared vs informed decision making
• Different (not always better) relationships
• Physician adoption of e-communication low

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Issues in CHI

• Privacy and security of health information
• Unequal access to information technology
• Quality of Web-based information

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Privacy and Security

• HIPAA
• Demand for access
• Masys, D., Baker, D., Butros, A., Cowles,
  K.E. (2002). Giving patients access to their
  medical records on the Internet the PCASSO
  experience. Journal of the American Medical
  Informatics Association, 9, 181-191

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Unequal Access to IT

• Digital divide is narrowing
• May have greatest benefit to underserved
• Gustafson, D.H., et al. (2001). Effect of
  computer support on younger women with breast
  cancer. Journal of General Internal Medicine, 16,
  435-445.

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Quality of Web-based information

• Most consumers (86) and health care
  professionals concerned about reliability of
  online information
• Systematic review of 79 studies concluded quality
  is problem
• Published criteria may not correlate with quality
  and accuracy
• Eysenbach, G, Powell, J., Kuss, O., Sa, ER.
  (2002) Empirical studies assessing the quality of
  health information for consumers on the world
  wide web. JAMA, 287, 2691-700.
• Fallis, D., Fricke, M. (2002). Indicators of
  accuracy of consumer health information on the
  Internet. JAMIA, 9, 73-79.

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Health Literacy and IT

• Degree to which individuals can obtain, process,
  and understand the basic health information and
  services they need to make appropriate health
  decisions
• 90 million Americans have difficulty
  understanding and using health information
• Bohlman LN, Panzer AM, Kindig DA. (2004).
  Health Literacy A Prescription to End Confusion.
  Washington DC The National Academies Press.

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Health Literacy and IT

• Multimedia and interactivity to reduce reading
  burden
• Targeting and tailoring information enhances
  message processing and information retention
• Kreuter, MW., Wray, RJ. (2003). Tailored and
  targeted health communication Strategies for
  enhancing information relevance. AJHB, 27,
  S227-232.

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CHI Exemplar

• Using Interactive Voice Response Technology to
  Enhance Treatment of Nicotine Dependence

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Purpose

• Develop and test an integrated system using
  interactive voice response (IVR) technology and
  tailored telephone counseling for relapse
  prevention in smoking cessation

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Relapse Prevention System

• Automated risk assessment
• Computerized telephone calls
• Relational database storage
• Automated query to identify at-risk participants
• Tailored telephone counseling
• Trained live counselors
• Standardized phone counseling protocol

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Automated Risk Assessment

• IVR technology
• Prospective calls for first 12 weeks after quit
  attempt
• Monitor smoking cessation progress
• Provide brief motivational messages
• Pre-recorded question set
• Responses entered via telephone keypad and
  recorded to database

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Tailored Telephone Counseling

• Calls triggered by IVR assessment data
• Any smoking reported
• Stress, mood, craving gt 3 on 5 point scale
• Missed 2 consecutive automated calls
• Trained counselors followed standard counseling
  protocol

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Methods

• Two-group comparison design
• Inner-city health system
• Group cessation program
• Baseline data collection
• Demographic data
• Smoking history
• Anxiety, depression, nicotine dependence,
  self-efficacy
• Six month follow-up
• 7-day point prevalence abstinence
• 6 month prolonged abstinence

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Intention to Treat Analysis

• Significantly different between treatment groups
  using Fishers Exact test.
• Note Abstinence was biochemically validated.

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Survival Analysis

• Log-rank test comparing time to relapse
  significantly different (p .0427)
• Median time to relapse
• 60 days (intervention) vs. 21 days (control)

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Dose Affect of Automated Calls
Significantly different between groups using
Logistic Regression and Wilcoxon Rank Sum test
p lt.05.