Using Patient Navigation to Impact Trust in the Health Care System ARANTZA RODRIGUEZ PUERTOS MENTOR: CARMEN E. GUERRA, M.D., M.S.C.E., F.A.C.P.
Introduction Data and Methods Outline Key Findings and Discussion Lessons Learned Q&A 2
Introduction Data and Methods Breast Cancer Detection and Outcomes Patient Navigation Program Project Overview Health Care System Distrust (HCSD) Scale Outline Key Findings and Discussion Lessons Learned Q&A 3
Early detection leads to higher survival rates 5 year survival rate: 99% for Stage I vs. 22% for Stage IV ¹ Breast Cancer Detection and Outcomes However, there is an underutilization of screening services ² In the US, 65.3% (2015) of women age 40+ had a mammogram within the past 2 years Insured (69.7%) vs. Uninsured (30%) 1. CANCER.NET 2017 2. CDC: HEALTH, UNITED STATES 2016 Compared to Whites, the Black and Hispanic populations have lower odds of utilizing screening mammography services. ³ 3. AHMED. T. AHMED, ET.AL. RACIAL DISPARITIES IN SCREENING MAMMOGRAPHY IN THE UNITED STATES: A SYSTEMATIC REVIEW AND META-ANALYSIS. 2017 4
Navigation Program Patient navigation was a concept first proposed by Dr. Harold Freeman in 1990 Main goal: save lives from cancer by Educating about the need for breast examinations Providing access to breast cancer screening services Making sure that woman with positive findings receive a timely diagnosis and treatment The most important role of Patient Navigation is to assure that an individual with a suspicious cancer-related finding will receive timely diagnosis and treatment. Through dozens of RCT, Patient Navigation Programs have shown that they effectively reduce disparities in access to cancer screening tests across different populations, tests and health care systems 1. A MODEL PATIENT NAVIGATION PROGRAM BY DR. HAROLD FREEMAN 5
Penn s Patient Navigation Program Where? Penn Medicine Breast Health Initiative (PMBHI) at the Abramson Cancer Center When? Established in 2014 What? Free breast cancer screening services and assistance (breast health education, scheduling appointments, transportation, translators) Who? Un/underinsured woman in the Philadelphia area ages 40-64 Why? Reduce disparities in breast cancer detection and mortality among underserved populations by overcoming access barriers to screening services 6
PMBHI s Navigation Program Services Provided Total patients served Screening mammograms Diagnostic mammograms Breast biopsy N 1022 833 325 64 Diagnosed Breast Cancer Stage 0 Stage I Stage II Stage 3 Stage 4 Total N 3 3 6 2 2 16 7
Project Overview Research Question Hypothesis Does a Patient Navigation Program for breast cancer screening impact trust in the Health Care System for un/underinsured women in the Philadelphia area? Patient Navigation Program increases trust in the Health Care System. 8
Health Care System Distrust (HCSD) Scale¹ Why focus on HCSD? Suggested as a barrier for seeking medical care, adhering to preventive health care, and participating in medical research 9 questions measured on a 5 point Likert Scale Scores ranged from 9-45 High scores meant high level of HCSD Measured trust on two dimensions: technical competence and value congruence (honesty, motives, equity) JUDY A. SHEA, ET.AL. DEVELOPMENT OF A REVISED HEALTH CARE SYSTEM DISTRUST SCALE. 2008 9
Health Care System Distrust (HCSD) Scale¹ JUDY A. SHEA, ET.AL. DEVELOPMENT OF A REVISED HEALTH CARE SYSTEM DISTRUST SCALE. 2008 10
Introduction Outline Data and Methods Key Findings and Discussion Methods Patient Enrollment Flowchart Patient Demographics Lessons Learned Q&A 11
Methods Recruitment New Patients of the Navigation program were recruited between 4/8/2016 to 8/14/2017 By phone or met at their appointment All of the patients answered a demographics survey Enrollment Patients enrolled answered the HCSD Scale pre and post participation in Navigation Program Data Analysis My role was to perform a secondary data analysis Data was analyzed using Stata 15.0 Multivariate analysis controlling for age, race/ethnicity, education, income, and mammography outcome. 12
Flowchart of Patient Enrollment 13
Patient Demographics (n=65) Demographics (n=65) Mean Age 51 Race Black 13(20%) Hispanic 26(40%) Other 22(34%) White 4(6%) Interpreter Yes 44(68%) No 21(32%) Education <than HS 36(55%) >than HS 25(36%) Income <than $15,000/year 40(64%) >than $15,000/year 25(36%) 14
Introduction Data and Methods Outline Key Findings and Discussion Lessons Learned General Data Analysis Multiple Regression Model Findings and Conclusions Limitations Q&A 15
Preliminary General Data Analysis The difference in scores (post-pre): Was on average -0.75 points (5.67=st. dev.) lower for the post test than the pre test Ranged from -24 to 10 points T-test between pre score vs. post score not significantly different (p=0.203) Not significant either for pre competence vs. post competence (p=0.0788) and pre values vs. post values (p=0.487) So, overall there was no significant change in trust. But 16
Preliminary Multivariate Analysis Dependent variable: Difference in score (post test pre test) Controlled for suspicious mammography result, race, age, use of interpreter and education AND Variable Suspicious Diagnosis Difference in Distrust Score -5.27* (p=-.042) Black -2.21 Hispanic 2.96 Other 1.46 Age 0.07 Used an interpreter -2.19 Had an education level lower than high schol -0.83 Demographics For Suspicious (n=6) Total (n=65) Mean Age 45.5 51 Race Black 0 13(20%) Hispanic 4(67%) 26(40%) Other 2 (33%) 22(34%) White 0 4(6%) Interpreter Yes 4(67%) 44(68%) No 2 (33%) 21(32%) Education <than HS 2(33%) 36(55%) >than HS 4(67%) 25(36%) Income <than $15,000/year 5(83%) 40(64%) >than $15,000/year 1(17%) 25(36%) 17
Subgroup analysis by BIRADS Category demonstrated that for patients with a suspicious mammography diagnosis distrust in the health care system decreased by 5.27 points Women with a suspicious diagnosis had more encounters with the navigator and with the navigation program as a whole One of the first studies that documents that patient navigation increases trust in the health care system Findings and Conclusions Significant finding because a lot of literature mentions that trust is really difficult to change 18
Limitations Additional variability from use of translators from 67% of patients Missing data given that patients had to be contacted at two different points Conclusions are limited to overall health care system distrust rather than individual provider distrust Small sample size (n=6) Single urban institution and geographic location (HUP, Philadelphia) 19
Introduction Data and Methods Outline Key Findings and Discussions Lessons Learned Q&A Stata Analysis Research Medicine Business 20
Lessons Learned Stata analysis Use research skills to develop programs that help underserved populations Medicine as a way of saving a life Adequate financial management is key for the success of such programs 21
Acknowledgements Dr. Carmen Guerra Patricia Hernandez Emily Verderame Andrea Nicholson Brenda Bryant Dr. Frank Leone Joanne Levy and Safa Browne SUMR Cohort 22
Q&A 23