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

Anna M. McDaniel, DNS RN FAAN

Indiana University

November 21, 2006

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

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 

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, 

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.

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”

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

CHI Continuum

Degree of Consumer Autonomy

Provide information

Access to personal medical information

Communicate with providers

Obtain education/ information/treatment

Give/receive support

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

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

Issues in CHI
        - Privacy and security of health information
        - Unequal access to information technology
        - Quality of Web-based information

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

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.

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) Empoirical studies assessing the qulaity 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.

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.

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.

CHI Exemplar

Using Interactive Voice Response Technology to Enhance Treatment of Nicotine Dependence

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

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

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

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

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 

Intention to Treat Analysis
        - Significantly different between treatment groups using Fisher’s Exact test.  
        - Note:  Abstinence was biochemically validated.
| Variable                           | N (%) or Median (Range) |
|------------------------------------|-------------------------|
| 6 Months                           | Intervention(n = 60)    | Control(n = 38) | p value |
| 7 Day Point-Prevalence Abstinence* | 23 (38%)                | 6 (16%) | .023 |
| Prolonged Abstinence               | 19 (32%)                | 6 (16%) | .098 |

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

“Dose” Affect of Automated Calls

* Significantly different between groups using Logistic Regression and  Wilcoxon Rank Sum test  p <.05.  
| 6 Months                            | N  | M (SD)    | OR        | 95% CI |
|-------------------------------------|----|-----------|-----------|--------|
| Prolonged Abstinence *              | No | 65        | 6.7 (2.5) | 0.71   | (0.53-0.94) |
| Yes                                 | 19 | 8.2 (1.8) |
| 7 Day Point-Prevalence Abstinence * | No | 61        | 6.5 (2.5) | 0.64   | (0.48-0.85) |
| Yes                                 | 23 | 8.4 (1.7) |