Table 3 Overview of data collection methods and analysis
Aim | Data sources | Analysis |
|---|---|---|
Aim 1: Implementation: To field the IPHR and evaluate use in terms of: | •IPHR/PHR database to measure which patients use the IPHR or PHR, and when and how often they use it | •Percent of approached practices that agree to use the IPHR (Adoption) |
•Percent of patients age 18–75 with a visit who create an IPHR account in months 1–12 (Reach) and 13–36 (practice-level Maintenance) (monthly repeated measures analysis) | ||
-Reach | ||
•EHR database to measure the number of potential IPHR users (denominator) | ||
- Adoption | ||
- Implementation | •Field notes to gather quantitative and qualitative insights on practice-level Adoption | •Percent of users who use the IPHR after 6 months (patient-level Maintenance) |
- Maintenance | •Mixed methods analysis of (quantitative) practice and clinician variation in Reach (two-level mixed-effects logistic regression) and (qualitative) consistency, variation, and fidelity of IPHR delivery (immersion/crystallization analysis of transcripts and diaries) (Implementation) | |
•Network records to measure practice (e.g., size) and clinician characteristics (e.g., age) | ||
•Learning collaborative transcripts, practice survey, practice diaries, and patient interviews to assess IPHR implementation, including consistency and adaptation, and to qualitatively assess Reach, Effectiveness, and Maintenance | ||
Scalability | Data sources and analysis similar to phase 1 except phase 2 will not include collecting and analyzing learning collaborative transcripts, practice diaries, site visits, or patient interviews | |
Aim 2: To compare the Effectiveness of the IPHR vs. traditional PHR functions | •EHR database to measure delivery of recommended cancer screening tests | •Percent of patients up-to-date with all indicated cancer screening for all practice patients (intention to treat) and for PHR users (sub-group) (two-level logistic regression) |
•IPHR/PHR database to identify users | ||
•Shared decision-making outcomes (knowledge, communication, decisional conflict, and decision control) (three-level generalized mixed-effects regression) | ||
•Patient survey of 4,000 randomly selected patients to measure elements of shared decision-making | ||
•Patient, practice, and clinician facilitators and barriers associated with Effectiveness (mixed-method analysis) | ||
•Learning collaborative transcripts, practice survey, practice diaries, and patient interviews to explore perceptions | ||
Aim 3: Disparities Assessment: Difference in use, effect and perception of technology for disadvantaged populations | •EHR database to identify at risk patients (minorities and Medicaid beneficiaries) and to measure delivery of recommended cancer screening tests | •Comparison of Reach and Effectiveness for the disadvantaged versus general population (two-level mixed-effects logistic regression) |
•Patient interviews to understand technology barriers and needs; technology impact; and unique issues for disadvantaged patients | ||
•IPHR/PHR database to stratify levels of use by minority and Medicaid status | ||
•Patient interviews | ||