A10.2 Collecting Using and Evaluating_Bennett, Abuayyash and Laplante by Alliance for Healthier Communities

CFPC Conflict of Interest

Disclosure of Commercial Support Presenter Disclosure Presenter:

Bennett-AbuAyyash, Caroline and LaPlante, Nancy

Relationships with commercial interests: • • • •

Grants/Research Support: None Speakers Bureau/Honoraria: None Consulting Fees: None Other: None

Collecting, Using, and Evaluating Patient Demographic Data Caroline Bennett-AbuAyyash Nancy LaPlante Prevent More to Treat Less: Public Health and Primary Health Care Together 4 June 2014

Todayâ&#x20AC;&#x2122;s objectives 1. Enhance skills to collect personal demographic information from patients/clients 2. Learn key approaches for evaluating demographic data quality 3. Explore how patient/client demographic data can be applied toward health equity

Workshop Design: Evidence-based Approach • Knowledge and skills in this workshop are researchbased and validated through: – “Measuring Health Equity in TC LHIN Hospitals” – “Community Health Centre Socio-Demographic Data Collection Pilot” • Both initiatives adopted 8 demographic questions: Language

Born in Canada

Gender

Sexual Orientation

Race/ Ethnicity Income

Disability # ppl this income supports

Objective: Enhance skills to collect personal demographic information from patients/clients

Defining Health Equity Equity in health care refers to ensuring quality care and best outcomes regardless of race, religion, language, income or any other individual characteristic

Quality care isâ&#x20AC;Ś Timely, Effective, Efficient, Person-centred, Safe, EQUITABLE

Demographic Data for ‘tracking health equity’ • Track and identify who we serve • Assess fit between needs and available services • Promote health equity: – Embed as a quality indicator – Identify inequities & plan interventions – Provide patient-focused care

Data Collection Process 1. Approaching client with questions – Be mindful of barriers around language and literacy

2. Remember to refer to resources available such as: – – – – –

Staff Asking Aid Poster Pamphlet Laminated questions with numbered options Glossary of term

Ready to Talk About… • WHY: Purpose of demographic data collection

“Find out who we serve” “Plan and deliver tailored care” “Best outcomes for all”

Ready to Talk About… • WHY: Purpose of demographic data collection • WHO: Who can see the data – Explain who this information (or pieces of it) will be visible to – Explain that if shared for research, it will only be done by grouping all patient information together and taking out any pieces that would identify them i.e. it can’t be traced back to a single individual

Ready to Talk About… • WHY: Purpose of demographic data collection • WHO: Who can see the data • Options around (not) responding and (not) participating – Voluntary – Prefer not to answer – Do not know

Useful illustrations WHO Only visible to those taking care of you while you’re here

WHY When we know who you are, we can serve your unique needs better

PARTICIPATION This will take a few minutes. It’s completely voluntary, so you can choose ‘prefer not to answer’ to any of questions.

Illustration: Best Practices  What did you see happening?  How do you evaluate the interaction with the patient?  What did the interviewer specifically say or do that encouraged the patient to answer the questions?

Time for Practice! • • • •

Review resources Pair up, one person plays patient then switch roles 3 Minutes to ask all questions Please review Practice Exercise sheet

Provide Feedback    

How well is the question explained? What was done well? Can anything be improved? What advice do you have?

Objective:

Explore key approaches for evaluating demographic data quality

The First Next Step: Data Quality • What is “quality data”? Assessment or examination of the ability to use data for its intended purpose in a given context • Why assess data quality?

Data

Information

Decisions

CIHI Data Quality Framework Timeliness

• Accessibility • Documentation • Interpretability

Usability

• Data dictionary Standards • Standardization • Linkage • Equivalency • Historical Comparability

• Data Currency at the Time of Release • Documentation Currency

Relevance

• Adaptability • Value

Data Quality

Comparability

Accuracy

• Coverage • Capture and Collection • Unit Non-Response • Item Non-Response • Measurement Error • Edit and Imputation • Processing and Estimation

Data Quality Indicators • Participation Rates – Percentage of clients who consent • Item Response Rates – Percentage of “meaningful responses” • Feedback: Staff & Clients

Data Quality Indicators • Participation Rates – Percentage of patients who consent • Item Response Rates – Percentage of “meaningful responses”

All responses, including ‘Do not know’, ‘Prefer not to answer’ and ‘Other’ can be a rich source of information.

Data Quality Indicators • Participation Rates – Percentage of patients who consent • Item Response Rates – Percentage of “meaningful responses” • Qualitative Feedback: From staff & patients

Type of error High rate of nonparticipation, where patient opts out of the 8 questions

Data Quality: Sources of Errors

Data Quality Dimension Affected Relevance Accuracy: Completeness Usability

Low item response rates Comparability (e.g. low response rates for Accuracy: Gender) Completeness Relevance Answer/response fails to capture information about client Not all clients are included Information cannot be accessed for use

Potential source

Data Collection • Client not asked the questions • Client asked but doesn’t want to participate

•

Data Entry/Storage Data not entered Not entered consistently and therefore not usable nor valuable for planning Data not entered

•

Client not asked the question; staff need to understand the importance of these questions Client does not understand the item

Accuracy: Coverage , Measurement Error

• •

The question is not clear Translation is incorrect

Data incorrectly entered

Relevance

•

Staff do not use data, so they do not understand its value Information not collected at right time nor updated on a regular basis Information is not available to the centre for use

N/A

Usability: Accessibility • Timeliness •

Data not accurate and therefore not comparable over time

Data stored in inaccessible space Data not current

Strategies for improving data quality • • • •

Consistent monitoring Engage department(s)/program(s) Follow up training USE THE DATA!

Wrap up: Data Quality Checklist ď ą Have protocols for gathering data on participation rates ď ąHave channels for receiving staff and patient feedback ď ąHave plans for consistent monitoring

Reflective Group Exercise Discuss with the person next to you: â&#x20AC;˘ What is the biggest issue that may (or currently does) affect demographic data quality in your organization? â&#x20AC;˘ What are some strategies to address it? Time: 5 minutes

Objective:

Understand how patient/client demographic data can be applied toward health equity

Demographic Data Use: Profiling Patients/Clients Income Distribution

Top 5 Client Ethnicities

70% 60%

Caribbean

32%

50%

Black

24%

40%

African

15%

Latin American

11%

30% 20% 10% 0%

South Asian

Hispanic

*Source: Tharao, W. (2013, September). Beyond Reporting: Using Data to Achieve Health Equity. Presented at Measuring Health Equity: Digging into the Data Symposium in Toronto, ON.

Demographic Data Use: Profiling Patients/Clients Primary Diagnosis FY 2012-2013 SOUTH ASIAN

BLACK CARIBBEAN

Substance Related Disorders 19.2%