Effect of continuity of care on hospital utilization for seniors with multiple medical conditions in an integrated health care system.

PURPOSE: Lower continuity of care has been associated with higher rates of adverse outcomes for persons with multiple chronic medical conditions. It is unclear, however, whether this relationship also exists within integrated systems that offer high levels of informational continuity through shared electronic health records. METHODS: We conducted a retrospective cohort study of 12,200 seniors with 3 or more chronic conditions within an integrated delivery system. Continuity of care was calculated using the Continuity of Care Index, which reflects visit concentration with individual clinicians. Using Cox proportional hazards regression permitting continuity to vary monthly until the outcome or censoring event, we separately assessed inpatient admissions and emergency department visits as a function of primary care continuity and specialty care continuity. RESULTS: After adjusting for covariates (demographics; baseline, primary, and specialty care visits; baseline outcomes; and morbidity burden), greater primary care continuity and greater specialty care continuity were each associated with a lower risk of inpatient admission (respective hazard ratios (95% CIs) = 0.97 (0.96, 0.99) and 0.95 (0.93, 0.98)) and a lower risk of emergency department visits (respective hazard ratios = 0.97 (0.96, 0.98) and 0.98 (0.96, 1.00)). For the subgroup with 3 or more primary care and 3 or more specialty care visits, specialty care continuity (but not primary care continuity) was independently associated with a decreased risk of inpatient admissions (hazard ratio = 0.94 (0.92, 0.97)), and primary care continuity (but not specialty care continuity) was associated with a decreased risk of emergency department visits (hazard ratio = 0.98 (0.96, 1.00)). CONCLUSIONS: In an integrated delivery system with high informational continuity, greater continuity of care is independently associated with lower hospital utilization for seniors with multiple chronic medical conditions. Different subgroups of patients will benefit from continuity with primary and specialty care clinicians depending on their care needs.

Assessment of morbidity over time in predicting health outcomes.

BACKGROUND: Administratively derived morbidity measures are often used in observational studies as predictors of outcomes. These typically reflect a limited time period before an index event; some outcomes may be affected by rate of morbidity change over longer preindex periods. OBJECTIVES: The aim of the study was to develop statistical models representing the trajectory of individual morbidity over time and to evaluate the performance of trajectory versus other summary morbidity measures in predicting a range of health outcomes. METHODS: From a retrospective cohort study of integrated health system members aged 65 years or older with 3 or more common chronic medical conditions, we used available diagnoses for up to 10 years to examine associations between variations of the Charlson Comorbidity Index (CCI, Quan adaptation) and health outcomes. A linear mixed effects model was used to estimate the trajectory of individual CCI over time; estimated parameters describing individual trajectories were used as predictors for health outcomes. Other variations of CCI were: a "snapshot" measure, a cumulative measure, and actual baseline and rate of change. Models were developed in an initial cohort for whom we had survey data, and verified in a larger cohort. RESULTS: Among 961 surveyed members and 13,163 members of a secondary cohort, cumulative and snapshot measures provided best fit and predictive ability for utilization outcomes. Incorporating trajectory resulted in a slightly better model for self-reported health status. CONCLUSIONS: Modeling longitudinal morbidity trajectories did not add substantially to the association between morbidity and utilization or mortality. Standard snapshot morbidity measures likely sufficiently capture multimorbidity in assessing these outcomes.

Association of patient-centered outcomes with patient-reported and ICD-9-based morbidity measures.

PURPOSE: Evaluating patient-centered care for complex patients requires morbidity measurement appropriate for use with a variety of clinical outcomes. We compared the contributions of self-reported morbidity and morbidity measured using administrative diagnosis data for both patient-reported outcomes and utilization outcomes. METHODS: Using a cohort of 961 persons aged 65 years or older with 3 or more medical conditions, we explored 9 health outcomes as a function of 4 independent variables representing different types of morbidity measures: International Classification of Diseases, Ninth Revision (ICD-9), a self-reported weighted count of conditions, and self-reported symptoms of depression and of anxiety. Outcomes varied from self-reported health status to utilization. Depending on the outcome measure, we used multivariate linear, negative binomial, or logistic regression, adjusting for demographic characteristics and length of enrollment to assess associations between dependent and all 4 independent variables. RESULTS: Higher morbidity measured by ICD-9 diagnoses was independently associated with less favorable levels of 7 of the 9 clinical outcomes. Higher self-reported disease burden was significantly associated with less favorable levels of 8 of the outcomes, controlling for the 3 other morbidity measures. Morbidity measured by diagnosis code was more strongly associated with higher utilization, whereas self-reported disease burden and emotional symptoms were more strongly associated with patient-reported outcomes. CONCLUSIONS: A comprehensive assessment of morbidity requires both subjective and objective measurement of disease burden as well as an assessment of emotional symptoms. Such multidimensional morbidity measurement is particularly relevant for research or quality assessments involving the delivery of patient-centered care to complex patient populations.

No date for the PROM: the association between patient-reported health events and clinical coding in primary care.

OBJECTIVE: It is unclear whether data from patient-reported outcome measures (PROMs) are captured and used by clinicians despite policy initiatives. We examined the extent to which fall risk and urinary incontinence (UI) reported on PROMS and provided to clinicians prior to a patient visit are subsequently captured in the electronic medical record (EMR). Additionally, we aimed to determine whether the use of PROMs and EMR documentation is higher for visits where PROM data was provided to clinicians. DESIGN: We conducted a cross-sectional patient-reported risk assessment survey and semi-structured interviews with clinicians to identify themes related to the use of PROMs. SETTING: Fourteen primary care clinics in the US (eight intervention and six control clinics), between October 2013 and May 2015. PARTICIPANTS: Primary care clinicians and older adult (≥66 years) patients completing a 46-item health risk assessment, including PROMs for fall risk and UI. INTERVENTION: Risk assessment results provided to the clinician or nurse practitioners prior to the clinic visit in intervention clinics; data was not provided in control clinics. MAIN OUTCOME: 1) Agreement between ICD-9 codes of fall risk or UI in the EMR and patient-reports, and 2) clinician experience of PROMs use and impact on coding. RESULTS: A total of 505 older adult patients were included in the study, 176 at control clinics and 329 at intervention clinics. While patient reports of fall risk and UI were readily captured by PROMs, this information was only coded in the EMR between 3% - 14% of the time (poor Kappa agreement). Intervention clinics performed slightly better than control clinics. Clinician interviews (n = 16) revealed low use of PROMs data with multiple barriers cited including poor access to data, high quantity of data, interruption to workflow, and a lack of training on PROMs. CONCLUSIONS: Current strategies of providing PROMs data prior to clinic visits may not be an effective way of communicating important health information to busy clinicians; ultimately resulting in underuse. Better systems of presenting PROMs data, and clinician training on the importance of PROMs and their use, is needed.

'The Hand on the Doorknob': Visit Agenda Setting by Complex Patients and Their Primary Care Physicians.

BACKGROUND: Choosing which issues to discuss in the limited time available during primary care visits is an important task for complex patients with chronic conditions. DESIGN, SETTING, AND PARTICIPANTS: We conducted sequential interviews with complex patients (n = 40) and their primary care physicians (n = 17) from 3 different health systems to investigate how patients and physicians prepare for visits, how visit agendas are determined, and how discussion priorities are established during time-limited visits. KEY RESULTS: Visit flow and alignment were enhanced when both patients and physicians were effectively prepared before the visit, when the patient brought up highest-priority items first, the physician and patient worked together at the beginning of the visit to establish the visit agenda, and other team members contributed to agenda setting. A range of factors were identified that undermined the ability of patient and physicians to establish an efficient working agenda: the most prominent were time pressure and short visit lengths, but also included differing visit expectations, patient hesitancy to bring up embarrassing concerns, electronic medical record/documentation requirements, differences balancing current symptoms versus future medical risk, nonactionable items, differing philosophies about medications and lifestyle interventions, and difficulty by patients in prioritizing their top concerns. CONCLUSIONS: Primary care patients and their physicians adopt a range of different strategies to address the time constraints during visits. The primary factor that supported well-aligned visits was the ability for patients and physicians to proactively negotiate the visit agenda at the beginning of the visit. Efforts to optimize care within time-constrained systems should focus on helping patients more effectively prepare for visits. Physicians should ask for the patient's agenda early, explain visit parameters, establish a reasonable number of concerns that can be discussed, and collaborate on a plan to deal with concerns that cannot be addressed during the visit.

Algorithm to Identify Systemic Cancer Therapy Treatment Using Structured Electronic Data.

PURPOSE: With the shift in the majority of oncology clinical care in the United States from paper records to electronic health records, researchers need efficient and validated processes to obtain accurate data about the entire treatment history of patients diagnosed with cancer. The objective of this study was to develop and validate an algorithm that is agnostic to the source of data but that can identify specific regimens in the entire course of systemic therapy treatment for patients diagnosed with breast, colorectal, or lung cancer. METHODS: A cohort of patients with incident breast, colorectal, and lung cancer were randomly distributed into six groups. The algorithm was iteratively modified, and the performance was assessed until no additional modifications could be identified in the first three groups. The performance of the algorithm was confirmed in the three groups that remained. RESULTS: The final model produced ranges of sensitivity between 97.2% and 100% for first-course systemic therapy across all cancers, with a false-positive rate of 0%. The algorithm matched the exact number of courses and the exact regimens of systemic therapy agents as captured by infusion, pharmacy, and procedure electronic medical record data for all courses of therapy 88% to 100% of the time. CONCLUSION: Use of our validated algorithm that characterizes entire courses of systemic therapy treatment in patients diagnosed with breast, colorectal, and lung cancer will allow researchers in a variety of settings to conduct comparative effectiveness studies related to the uptake, safety, outcomes, and costs associated with the use of both novel and standard regimens.

Association between Continuity of Care and Health-Related Quality of Life.

BACKGROUND: Patient-reported outcomes (PROs) are considered potential quality metrics for patients with multiple chronic medical conditions (MCC). Although continuity of care (COC) is an essential MCC care process, the association between common PROs and COC is unknown. METHODS: We assessed baseline and two-year follow-up self-reported health status, physical, and emotional well-being, and COC in seniors with MCC. Using mixed effects models with repeated measures adjusting for age, gender, and morbidity, we assessed each outcome as a function of COC. RESULTS: Of 2,078 seniors, 961 completed the initial survey and 806 completed follow-up. On a 0-100 scale, mean (sd) baseline self-reported health status, physical well-being, and emotional well-being were 48.7 (22.0), 36.4 (11.4), and 54.8 (9.0). On a 0 to 1 scale, mean baseline and 2-year COC were 0.24 (sd 0.22) and 0.22 (0.18). Follow-up self-reported health status, physical well-being, and emotional well-being were 48.8 (23.1), 36.5 (11.5), and 55.3 (8.8). In adjusted primary and secondary analyses using all available data, there were no associations between any outcomes and COC. CONCLUSION: Given the measurement burden of quality assessment, negative associations between potential quality metrics and care processes are informative. Systematic assessment of PROs can inform patient-centered MCC care. However, PRO scores should be used with caution as quality measures.

Using Electronic Health Record Data to Measure Care Quality for Individuals with Multiple Chronic Medical Conditions.

OBJECTIVES: To inform the development of a data-driven measure of quality care for individuals with multiple chronic conditions (MCCs) derived from an electronic health record (EHR). DESIGN: Qualitative study using focus groups, interactive webinars, and a modified Delphi process. SETTING: Research department within an integrated delivery system. PARTICIPANTS: The webinars and Delphi process included 17 experts in clinical geriatrics and primary care, health policy, quality assessment, health technology, and health system operations. The focus group included 10 individuals aged 70-87 with three to six chronic conditions selected from a random sample of individuals aged 65 and older with three or more chronic medical conditions. MEASUREMENTS: Through webinars and the focus group, input was solicited on constructs representing high-quality care for individuals with MCCs. A working list was created of potential measures representing these constructs. Using a modified Delphi process, experts rated the importance of each possible measure and the feasibility of implementing each measure using EHR data. RESULTS: High-priority constructs reflected processes rather than outcomes of care. High-priority constructs that were potentially feasible to measure included assessing physical function, depression screening, medication reconciliation, annual influenza vaccination, outreach after hospital admission, and documented advance directives. High-priority constructs that were less feasible to measure included goal setting and shared decision-making, identifying drug-drug interactions, assessing social support, timely communication with patients, and other aspects of good customer service. Lower-priority domains included pain assessment, continuity of care, and overuse of screening or laboratory testing. CONCLUSION: High-quality MCC care should be measured using meaningful process measures rather than outcomes. Although some care processes are currently extractable from electronic data, capturing others will require adapting and applying technology to encourage holistic, person-centered care.

Characteristics of Newly Enrolled Members of an Integrated Delivery System after the Affordable Care Act.

Of 89,289 newly enrolled non-Medicare members, 25.3% completed the Brief Health Questionnaire between 1/1/2014, and 8/31/2014. Of these, 3593 respondents were insured through Medicaid, 9434 through the individual health exchange, and 9521 through primarily commercial plans. Of Medicaid, exchange, and commercial members, 19.5%, 7.1%, and 5.3%, respectively, self-reported fair or poor health; 12.9%, 2.0%, and 3.3% of each group self-reported 2 or more Emergency Department visits during the previous year; and 8.1%, 4.3%, and 4.4% self-reported an inpatient admission during the previous year.

Competing risks of cancer mortality and cardiovascular events in individuals with multimorbidity.

BACKGROUND: Cancer patients with cardiovascular and other comorbidities are at concurrent risk of multiple adverse outcomes. However, most treatment decisions are guided by evidence from single-outcome models, which may be misleading for multimorbid patients. OBJECTIVE: We assessed the interacting effects of cancer, cardiovascular, and other morbidity burdens on the competing outcomes of cancer mortality, serious cardiovascular events, and other-cause mortality. DESIGN: We analyzed a cohort of 6,500 adults with initial cancer diagnosis between 2001 and 2008, SEER 5-year survival ≥26%, and a range of cardiovascular risk factors. We estimated the cumulative incidence of cancer mortality, a serious cardiovascular event (myocardial infarction, coronary revascularization, or cardiovascular mortality), and other-cause mortality over 5 years, and identified factors associated with the competing risks of each outcome using cause-specific Cox proportional hazard models. RESULTS: Following cancer diagnosis, there were 996 (15.3%) cancer deaths, 328 (5.1%) serious cardiovascular events, and 542 (8.3%) deaths from other causes. In all, 4,634 (71.3%) cohort members had none of these outcomes. Although cancer prognosis had the greatest effect, cardiovascular and other morbidity also independently increased the hazard of each outcome. The effect of cancer prognosis on outcome was greatest in year 1, and the effect of other morbidity was greater in individuals with better cancer prognoses. CONCLUSION: In multimorbid oncology populations, comorbidities interact to affect the competing risk of different outcomes. Quantifying these risks may provide persons with cancer plus cardiovascular and other comorbidities more accurate information for shared decision-making than risks calculated from single-outcome models. Journal of Comorbidity 2014;4:29-36.