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UNIVERSITY OF MISSOURI

Family & Community Medicine

Making it easier to do better: a team approach to providing primary care

for patients with chronic disease

Joseph LeMaster MD MPH

Steve Zweig MD MSPH

Robin Kruse PhD



Background

• Two-thirds of Medicare beneficiaries have multiple chronic illnesses

• Risk of death and hospitalization is high

• Between 2000 and 2030, this population will double but the US primary care workforce will increase only 4-5% during this period

• Primary care delivery is organized to manage acute illnesses, not preventing disease exacerbations or preserving function



What’s been done?

• “Patient navigator” developed for cancer patients in large systems

§ Adapted for those with chronic diseases

§ Care coordination: coordinating physician referrals, diagnostic investigation, treatments, monitoring disease severity and complications, promotion of self-management



What’s been done?

• “Patient navigator” for chronic disease works well in large systems

§ Usually on-site PA/Nurse practitioners using patient registries

§ Unclear how this translates to:

•Small, solo practices

•Other staffing models



Hypotheses for study series

• Hypotheses: Management of patients with chronic illnesses via nurse ‘navigators’:

§ Decreases mortality, hospitalizations and ED visits without increasing primary care visits.

§ Improves intermediate outcomes (BP, HbA1c, LDL cholesterol)

§ Can be carried out satisfactorily by staff with less training

§ Improves patients’ perceptions about their care experience



Study 1 : 5-year cohort elderly chronically ill

• Quasi-experimental prospective cohort of Medicare retirees

§130 navigator-managed, 249 controls, from 2 adjacent clinics

§Frequently seen (> 3 visits) during 1998

§5 years of follow-up

§Outcomes: mortality, utilization

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Deaths: 26.9% in the intervention group and 27.3% in the control group (p = 0.94). After controlling for age and sex there was no survival difference (p = 0.56).

Results: Mortality



Results: Fewer ED visits



Results: Fewer Urgent Care visits



Study 2: Factors associated with good self-care behavior

• Cross-sectional survey in 77 navigator-managed patients

§Study 1 intervention clinics (representative)

§Type 2 diabetes mellitus + > 1 other disease

§Outcomes: followed DM diet, exercised, measured BG



Study 2: Factors associated with good self-care behavior

§ Independent variables: • Co-morbidity (1-16 diseases)

• Self-rated understanding of diabetes

• Self-rated general and mental health (SF-36)

• Self-efficacy (Perceiv. Competence for DM)

• Self-rated social support (MOS-SSS)

• Health literacy (REALM, newest vital sign)

•Motivation (autonomous v. controlled)



Study 2: Descriptives

• Caucasian-75%

• Women-64%

• High school graduates-87%

• Less than 8th grade-4%

• Self-rated understanding of DM rx-53%

• Mental health (SF-36 MCS)-75 (SD 18)

• Social support (MOS-SSS)- 76 (SD 19)

• Mean No. chronic conditions- 4.6



Study 2: Outcomes



Study 2: Associations

• Only autonomous motivation associated with maintaining diet and glucose-monitoring

• No associations with exercise

• Healthcare organizational system not examined but motivational approach suggested



Study 3: One-year cohort Type 2 diabetes mellitus

• Quasi-experimental study in 6 rural primary care practices

§546 LPN navigator-managed (5 clinics), 302 controls (1 clinic)

§Diagnosis of type 2 DM in past 3 years

§Specific aim to engage low-income and minority patients



Study 3: One-year cohort Type 2 diabetes mellitus

• All LPN-navigators were full-time engaged in this work, and trained in ‘motivational interviewing’ to support autonomous motivation

§ Intermediate disease-related outcomes: HbA1c, LDL cholesterol, SBP, DBP



Change in HbA1c < 7



Change in BP < 130/80



Change in LDL < 100



Study 3 sub-study: community activism

• Community advisory boards in both Study 3 counties worked to engage low-income and minority residents

• Conducted “Working-on-Wellness” a 6-month campaign (contest) to increase fruit-&-veg consumption and daily exercise

• Let by local grocers, churches, newspapers



Study 3 sub-study: community activism

• Results:

§ 160 contestants joined, 128 remained

§ 68% increased fruit and vegetable intake and 78% increased daily exercise, both significantly.

§ Medical practice staff participated, but perceived the effort as unrelated to clinical functions



Study 4: Interviews with patients and providers, observations of provider-patient interactions

• Patients had overwhelmingly positive regard for the nurse navigators

• Appreciated availability of the navigator

• Perceived stronger partnership with healthcare provider team compared to prior healthcare experiences



• Both MDs, NPs and LPNs focus on out-of-range lab values & medication compliance

• No one made it a priority to help patients make diet and exercise goals or check progress on goals§ No place to document it in the EMR

Study 4: Interviews with patients and providers, observations of provider-

patient interactions



Study 4: Direct observations of provider-patient interactions/provider interviews

• Less-well trained staff (LPNs, MAs) CAN do this work, but the context MUST provide optimal support:

§Physician/health system buy-in

§Adequate time & space, and on-going training for LPN/MA

§When these are absent, nurse side-tracked, subverted or stolen



Implications

• Even optimal NP/PA-led ‘navigator’management does not reduce mortality or hospital admissions long-term, though reduces ED utilization

• Change in intermediate outcomes (LDL, BP, HbA1c) depends on behavior–change: self-care goals met/set!



Implications

• Connecting the community to clinics may be critical in engaging low-income and minority families and helping them establish a primary care “home”

CommunityResources

Health Care System

Self-Management Support

Clinical Information System

Decision Support

Delivery System Re-design

Informed, Activated Patient

Prepared, proactive

practice team



The PCMH opportunity

• HIT “Meaningful Use” = PCMH

(overlaps): § Will providing financial incentives without local, ongoing practice facilitation be

enough to drive transformation?

• How do patients perceive the process?§ TransforMed: patients did not rate PCMH practices highly

§ Ferrante et.al.:“High-tech” practices provide less preventive care than “high touch”practices



A Primary Care Extension Service would:

• Provide > 1 person/U.S. county as a “relational link” between practices and:

§ Vetted vendors/technicians/researchers with the aim of achieving PCMH and ‘meaningful use’ certification

§ community agencies and public health entities to improve PCP access and services for the whole population



A Primary Care Extension Service:

Let’s get it funded!



References

1. Rastkar R, Zweig S, Delzell JE, Jr., Davis K. Nurse care coordination of ambulatory frail elderly in an academic setting. Case Manager.2002;13(1):59-61.

2. Kruse RL, Zweig SC, Nikodim B, LeMaster JW, Coberly JS, Colwill JM. Nurse Care Coordination of Older Patients in an Academic Family Medicine Clinic: 5-Year Outcomes. J Clinical Outcomes Meas. 2010; 17 (5): 209-215.

3. Shigaki C, Kruse RL, Mehr D, Sheldon KM, Ge B, Moore C, LeMaster J. Motivation and diabetes self-management. Chronic Illn. 2010; 6: 202-214.

4. Shigaki CL, Moore C, Wakefield B, Campbell J, LeMaster J. Nurse partners in chronic illness care: patients' perceptions and their implications for nursing leadership. Nurs Adm Q. 2010;34(2):130-140.

5. Ferrante JM, Balasubramanian BA, Hudson SV, Crabtree BF. Principles of the patient-centered medical home and preventive services delivery. Ann Fam Med. 2010;8(2):108-116.

6. Grumbach K, Mold JW. A health care cooperative extension service: transforming primary care and community health. JAMA.2009;301(24):2589-2591.



Acknowledgements

• Steven Zweig MD MSPH (MU Family and Community Medicine Chair)

• David Mehr MD MS (Research Director)

• Robin Kruse PhD

• Richelle Koopman MD

• Karl Kochendorfer MD

• Rebecca Rastkar, RN, FNP

• Tamara Day RN

• Molly Vetter-Smith MS

• Jared Coberly, BS

Questions ?





Methods: Statistical analysis

• Compared age distribution and sex of participants using t-test and chi-square test, respectively

• Kaplan-Meier analysis produced unadjusted survival curves

• Compared survival between groups with Cox regression, including age and sex as covariates

• Compared outcome rates between groups with Poisson regression, including the natural logarithm of days in the study (offset variable),including age and sex as control variables.

• To determine if time since enrollment affected use of health services, we compared outcome rates between groups each year following enrollment, using the same regression techniques.

•



Methods: Outcome variables

• Outcomes from billing and hospital databases for 5-year period starting July 1, 1999.

• Variables: patient demographics, number of hospital and observation stays; hospital days; ED and urgent care visits; outpatient visits (to primary care and specialties).

• Date of death from administrative data and search of National Death Index

• Outcomes expressed per 1000 days observed§ Through June 30, 2004, date of last contact, or death, whichever came earliest.



Health care utilization

Measure Intervention Control P-value

ED visits .714 (.535 – .953) 1.04 (.859 – 1.27) 0.034

Urgent care visits .174 (.123 – .246) .426 (.362 – .502) <0.001

Reported as the mean/1000 patient-days (95% CI)



Limitations

• Not RCTs so limited ability to control for confounding/disease severity or generalize widely

• Studies 1-3 had slightly different populations so not 100% comparable

• Unable to measure health care utilization outside University of Missouri Health Care but no reason to expect difference between groups



Change in Annual Measurement (clinics)

Chi-square ∆ p between time intervals

Cholesterol, p = 0.003, HgbA1c, p = 0.002

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