Disclosure of Commercial Support CFPC Conflict of Interest

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

Today’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 research-based 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

Race/ Ethnicity Disability

Gender Sexual

Orientation Income # 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 is…

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”

• 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

Ready to Talk About…

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

better

This will take a few minutes. It’s

completely voluntary, so you can choose

‘prefer not to answer’ to any of questions.

Only visible to those taking care

of you while you’re here

Useful illustrations

WHY

WHO

PARTICIPATION

14

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?

Illustration: Best Practices

15

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

Usability

Comparability Accuracy

Relevance

Timeliness

Data Quality

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

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

• Adaptability• Value

• Accessibility• Documentation• Interpretability

• Data Currency at the Time of Release• Documentation Currency

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

Data Quality: Sources of Errors Type of error Data Quality

Dimension Affected Potential source

Data Collection Data Entry/Storage High rate of non-participation, where patient opts out of the 8 questions

Relevance Accuracy: Completeness Usability

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

participate

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

Low item response rates (e.g. low response rates for Gender)

Comparability Accuracy: Completeness Relevance

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

• Client does not understand the item

Data not entered Data not accurate and therefore not comparable over time

Answer/response fails to capture information about client

Accuracy: Coverage , Measurement Error

• The question is not clear • Translation is incorrect

Data incorrectly entered

Not all clients are included Relevance • Staff do not use data, so they do not understand its value

N/A

Information cannot be accessed for use

Usability: Accessibility Timeliness

• Information not collected at right time nor updated on a regular basis

• Information is not available to the centre for use

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

Have protocols for gathering data on participation rates

Have channels for receiving staff and patient feedback

Have plans for consistent monitoring

Reflective Group Exercise

Discuss with the person next to you: • What is the biggest issue that may (or currently

does) affect demographic data quality in your organization?

• 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

0%

10%

20%

30%

40%

50%

60%

70%

Income Distribution

Caribbean 32%

Black 24%

African 15%

Latin American 11%

South Asian 6%

Hispanic 4%

Top 5 Client Ethnicities

*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

BLACK CARIBBEAN

Sexual Disorders

4.0%

Substance Related

Disorders19.2%

Other Problems

6.1%Childhood Disorders

3.3%

Anxiety Disorder

5.9%

Unknown15.3%

Psychotic Disorders

28.3%

Developmental

Disorders2.1%

Mood Disorders

15.9%

SOUTH ASIAN

Anxiety Disorder

9.1%

Psychotic Disorders

16.0%

Other Problems

15.7%

Unknown14.7%

Substance Related

Disorders15.1%Sexual

Disorders5.3%

Mood Disorders

24.1%

Primary Diagnosis FY 2012-2013

*Source: Agic, B. (2013, September). Equity-Driven Service Planning and Delivery. Presented at Measuring Health Equity: Digging into the Data Symposium in Toronto, ON.

Community Health Centres Equity data Example…

Community Health Centres Equity data Example…

Beyond Reporting: Using Demographic Data to Make Linkages

Health Equity

- Cancer screening - Pre-natal programs - Diabetes management

- ED visits - Birth weight - Cancer survival - Re-admission rate - Drug dosage

- Referrals - Adherence to protocols

Access to Services

Health Outcomes

Healthcare Delivery

Using Demographic Data for Awareness and Education

Reflective Group Exercise

Discuss with the person next to you: • What can demographic data use look like in your

organization? • What would you expect to find? Time: 5 minutes

Wrap-up: Looking at the Big Picture

Health Equity

Planning Collecting

Data

Identifying Inequities Reporting

Additional Resources: • Mount Sinai Hospital & TC LHIN

website on demographic data collection www.torontohealthequity.ca

• HRET Disparities Toolkit http://www.hretdisparities.org/

• Robert Wood Johnson

Foundation http://www.rwjf.org/

THANK YOU!