Controlling Hypertension through Education and Coaching in Kidney Disease (CHECK-D): protocol of a cluster randomised controlled trial.

INTRODUCTION: Chronic kidney disease (CKD) affects 30 million Americans. Early management focused on blood pressure (BP) control decreases cardiovascular morbidity and mortality. Less than 40% of patients with CKD achieve recommended BP targets due to many barriers. These barriers include a lack of understanding of the implications of their diagnosis and how to optimise their health.This cluster randomised control trial hypothesises that the combination of early primary care CKD education, and motivational interviewing (MI)-based health coach support, will improve patient behaviours aligned with BP control by increasing patient knowledge, self-efficacy and motivation. The results will aid in sustainable interventions for future patient-centric education and coaching support to improve quality and outcomes in patients with CKD stages 3-5. Outcomes in patients with CKD stages 3-5 receiving the intervention will be compared with similar patients within a control group. Continuous quality improvement (CQI) and systems methodologies will be used to optimise resource neutrality and leverage existing technology to support implementation and future dissemination. The innovative approach of this research focuses on the importance of a multidisciplinary team, including off-site patient coaching, that can intervene early in the CKD care continuum by supporting patients with education and coaching. METHODS AND ANALYSIS: We will test impact of BP control when clinician-delivered education is followed by 12 months of MI-based health coaching. We will compare outcomes in 350 patients with CKD stages 3-5 between intervention and control groups in primary care. CQI and systems methodologies will optimise education and coaching for future implementation and dissemination. ETHICS AND DISSEMINATION: This study was approved by the University of Michigan Institutional Review Boards (IRBMED) HUM00136011, HUM00150672 and SITE00000092 and the results of the study will be published on ClinicalTrials.gov, in peer-reviewed journals, as well as conference abstracts, posters and presentations. TRIAL REGISTRATION NUMBER: NCT04087798.

The Prevalence of Periodontitis Among US Adults with Multimorbidity Using NHANES Data 2011-2014.

INTRODUCTION: Often misperceived as solely a dental disease, periodontitis is a chronic condition characterized by inflammation of the support structures of the tooth and associated with chronic systemic inflammation and endothelial dysfunction. Despite affecting almost 40% of US adults 30 years of age or older, periodontitis is rarely considered when quantifying the multimorbidity (the presence of 2 or more chronic conditions in an individual) burden for our patients. Multimorbidity represents a major challenge for primary care and is associated with increasing health care expenditure and increased hospitalizations. We hypothesized that periodontitis was associated with multimorbidity. METHODS: To interrogate our hypothesis, we performed a secondary data analysis of a population-based cross-sectional survey, the NHANES 2011 to 2014 dataset. The study population included US adults aged 30 years or older who underwent a periodontal examination. Prevalence of periodontitis in individuals with and without multimorbidity was calculated using likelihood estimates and adjusting for confounding variables with logistic regression models. RESULTS: Individuals with multimorbidity were more likely than the general population and individuals without multimorbidity to have periodontitis. However, in adjusted analyses, there was no independent association between periodontitis and multimorbidity. Given the absence of an association, we included periodontitis as a qualifying condition for the diagnosis of multimorbidity. As a result, the prevalence of multimorbidity in US adults 30 years and older increased from 54.1% to 65.8%. DISCUSSION: Periodontitis is a highly prevalent, preventable chronic inflammatory condition. It shares many common risk factors with multimorbidity but was not independently associated with multimorbidity in our study. Further research is required to understand these observations and whether treating periodontitis in patients with multimorbidity may improve health care outcomes.

Pharmacogenomic testing for mental health (Part I): documenting early adopter perceptions of use for eight scenarios.

Aim: We sought to understand how early adopters used pharmacogenomic (PGx) testing for treating depression and attention deficient hyperactivity disorder (ADHD). Patients & methods: We conducted a phone survey with prescribers who had previously ordered an Informed PGx (Progenity, Inc., MI, USA) test. Results: We identified 1037 prescribers in our sampling period. Respondents (n = 64) were predominantly female (61.5%) and in pediatrics (n = 42; 64.6%). PGx testing was used for multiple scenarios (mean 3.3 ± 1.6); the most common was after no response to medication was observed (80%; 51/64). Most respondents state that test results typically reveal an altered metabolizer status. Conclusion: PGx test results ordered by early adopters often reveal altered metabolizers which leads them to change the depression/ADHD medication regimen. Future work should evaluate the clinical utility of PGx testing for depression/ADHD treatment.

Pharmacogenomic testing for mental health (Part II): qualitative analysis of early adopter prescriber perceptions.

Aim: We sought to explore how early adopters use pharmacogenomic (PGx) testing for treating depression and attention-deficit/hyperactivity disorder. Patients & methods: Prescribers of the Informed PGx (Progenity, Inc., Ann Arbor, MI 48108, USA) test completed a phone survey assessing use of PGx testing for different scenarios. We conducted a qualitative thematic text analysis of transcribed audio recordings of open-ended responses (n = 62). Results: PGx testing was used when treating multiple comorbidities or resistant disease, and to ease patients' concerns with future therapy. Use of PGx testing is influenced by insurance coverage, interpretability of results and results turnaround time. Conclusion: Prescribers used PGx tests to modify medications for complex patients with depression, attention-deficit/hyperactivity disorder and other disorders to alleviate concerns related to adverse effects and lack of effectiveness.

Using Pharmacogenomic Testing in Primary Care: Protocol for a Pilot Randomized Controlled Study.

BACKGROUND: Antidepressants are used by primary care providers to treat a variety of conditions, including (but not limited to) depression and anxiety. A trial-and-error approach is typically used to identify effective therapy, as treatment efficacy and safety can vary based on the response, which is affected by certain gene types. Pharmacokinetic pharmacogenomic (PGx) testing provides phenotypic classification of individuals as poor, intermediate, extensive, and ultrarapid CYP450 metabolizers, providing information for optimal drug selection. OBJECTIVE: The objective of this pilot study is to examine the feasibility, acceptability, and preliminary effectiveness of PGx testing when used after starting a new antidepressant medication. METHODS: We are conducting a pilot study with physicians from 6 Department of Family Medicine clinics at the University of Michigan who are willing to use PGx test results to manage antidepressant medication use. From enrolled physicians, patients were recruited to participate in a 6-month randomized, wait-list controlled trial in which patient participants newly prescribed an antidepressant had PGx testing and were randomized equally to have the results released to their primary care physician as soon as results were available or after 3 months. Patients were excluded if they had been taking the antidepressant for more than 4 weeks or if they had undergone PGx testing in the past. Physician participants completed a baseline survey to assess demographics, as well as knowledge, feasibility, and acceptability of PGx testing for this population. At the conclusion of the study, physician participants will complete a survey to assess knowledge, satisfaction, feasibility, acceptability, perceived effectiveness, and barriers to widespread adoption of PGx testing. Patient participants will complete a baseline, 3-month, and 6-month assessment, and control patient participants will have an additional 9-month assessment. Data collected will include the reason for antidepressant use, self-reported medication adherence, side effects, patient health questionnaire 8-item depression scale, generalized anxiety disorder 7-item scale, 12-Item Short-Form Health Survey, work status or changes, and physician and emergency department visits. PGx knowledge and perceptions (including acceptability and feasibility) as well as demographic information will also be obtained. RESULTS: We recruited 23 physician participants between November 2017 and January 2019, and 52 patient participants between January 2018 and April 2019. Currently, all physician and patient participants have been recruited, and we expect data collection to conclude in January 2020. CONCLUSIONS: This study will examine the preliminary effectiveness of PGx testing after treatment initiation and determine the feasibility and acceptability of PGx testing for use in primary care. Through this study, we expect to demonstrate the benefit of PGx testing and lay the foundation for translating this approach into use within primary care. TRIAL REGISTRATION: ClinicalTrials.gov NCT03270891; https://clinicaltrials.gov/ct2/show/NCT03270891. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/13848.

Towards a Learning Health System to Reduce Emergency Department Visits at a Population Level.

High utilizers of the Emergency Department (ED) often have complex needs that require coordination of care between multiple organizations. We describe a Learning Health Systems (LHS) approach to reducing ED visits, in which an intervention is delivered to a cohort of high utilizers identified using population-level data and predictive modeling. We focus on the development and validation of a random forest model that utilizes electronic health record data from three health systems across two counties in Michigan to predict the number of ED visits each resident will incur in the next six months. Using 5-fold cross-validation, the model achieves a root-mean-squared-error of 0.51 visits and a mean absolute error of 0.24 visits. Using time-based validation, the model achieves a root-mean-squared error of 0.74 visits and a mean absolute error of 0.29 visits. Patients projected to have high ED utilization are being enrolled in a community-wide care coordination intervention using twelve sites across two counties. We believe that the repeated cycles of modeling and intervention demonstrate an LHS in action.

Improving the assessment of depression remission with the Remission Evaluation and Mood Inventory Tool.

OBJECTIVE: The Remission Evaluation and Mood Inventory Tool (REMIT) is a practical 5-item self-report measure of key positive mood states associated with recovering from depression, as distinct from depressive symptoms per se. The study goal was to identify a clinically useful threshold for interpreting REMIT responses in the context of mild to moderate depressive symptoms. METHODS: This was a secondary analysis of a cross-sectional dataset initially used to develop and validate the REMIT. Primary care patients being treated for depressive symptoms of either mild or moderate severity (n = 247 and 240, respectively) rated their perceived degree of depression remission prior to completing the Patient Health Questionnaire-8 (PHQ-8) and the REMIT. We summed the totals of the latter two measures to form the PHQ + REMIT index. RESULTS: Receiver Operating Characteristics analysis indicated that the PHQ + REMIT threshold ≥ 13 was associated with good sensitivity (92%) and acceptable specificity (43%) to the absence of patient-perceived remission. In contrast, the PHQ had only 21% specificity at this sensitivity level. Area under the curve was 0.815 (95% C.I.: 0.765-0.865), which was significantly greater than that of the PHQ-8 alone (area under the curve = 0.745, 95% C.I.: 0.691-0.805, p(diff) = 0.0002). Threshold performance was unaffected by adjustment for demographic characteristics and variation in remission percentage. Compared with standard symptom-based classification, using the REMIT reclassified 27% of mildly symptomatic patients as remitted. CONCLUSIONS: Using the REMIT with patients who have mild to moderate depressive symptoms improves the assessment of patient-perceived remission, which is indicated by a summed PHQ + REMIT index of less than 13. Longitudinal research is needed to test whether this broadened patient-centered approach to assessing remission improves clinical decision making and long-term outcomes.

Health information technology needs help from primary care researchers.

While health information technology (HIT) efforts are beginning to yield measurable clinical benefits, more is needed to meet the needs of patients and clinicians. Primary care researchers are uniquely positioned to inform the evidence-based design and use of technology. Research strategies to ensure success include engaging patient and clinician stakeholders, working with existing practice-based research networks, and using established methods from other fields such as human factors engineering and implementation science. Policies are needed to help support primary care researchers in evaluating and implementing HIT into everyday practice, including expanded research funding, strengthened partnerships with vendors, open access to information systems, and support for the Primary Care Extension Program. Through these efforts, the goal of improved outcomes through HIT can be achieved.

Clinical reminders designed and implemented using cognitive and organizational science principles decrease reminder fatigue.

BACKGROUND: Response rates to point-of-care clinical reminders typically decrease over time. We hypothesized that this "reminder fatigue" could be prevented by (1) applying sound human factors engineering and cognitive science principles in designing the reminder system, and (2) implementing the reminders with rigorous attention to organizational science principles. METHODS: This was a retrospective cohort enumeration from January 1, 2006, through July 31, 2012, in a set of 5 academically affiliated family medicine practices. We modeled the odds ratio of clinician action in response to a reminder according to the number of reminders issued during the encounter, the number of problems on the patient's problem list, patient age, and time (number of months since launch) using logistic regression with clustering by encounter. RESULTS: There were issued 988,149 reminders at 453,537 encounters during the sampling frame. Action was taken in response to 60.1% of reminders, and discussion or consideration was documented in another 26.8%. The odds ratios for action in response to reminders over time, by number of prompts during the encounter, and by number of problems were 1.01, 1.18, and 1.02, respectively. Key design features included issuing reminders only when a service was due, allowing clinicians to attend to reminders when doing so fit their workflow (vs forcing attention at a specific time), keeping reminders very short and simple (action item only, no explicative material), and a team meeting and buy-in process before each new reminder was implemented. CONCLUSIONS: Reminder fatigue over time, with increasing numbers of reminders and with increasing complexity of patients, is not inevitable. A reminder system designed and implemented in accordance with the principles of cognitive science and human factors engineering can prevent reminder fatigue.

Electronic health record functionality needed to better support primary care.

Electronic health records (EHRs) must support primary care clinicians and patients, yet many clinicians remain dissatisfied with their system. This article presents a consensus statement about gaps in current EHR functionality and needed enhancements to support primary care. The Institute of Medicine primary care attributes were used to define needs and meaningful use (MU) objectives to define EHR functionality. Current objectives remain focused on disease rather than the whole person, ignoring factors such as personal risks, behaviors, family structure, and occupational and environmental influences. Primary care needs EHRs to move beyond documentation to interpreting and tracking information over time, as well as patient-partnering activities, support for team-based care, population-management tools that deliver care, and reduced documentation burden. While stage 3 MU's focus on outcomes is laudable, enhanced functionality is still needed, including EHR modifications, expanded use of patient portals, seamless integration with external applications, and advancement of national infrastructure and policies.

Furthering the reliable and valid measurement of mental health screening, diagnoses, treatment and outcomes through health information technology.

INTRODUCTION: Measurement of mental health is challenging; however, many solutions may be found through the use of health information technology. METHOD: This article reviews current approaches to measuring mental health, focusing on screening, diagnosis, treatment, and outcomes. It then identifies several key areas in which health information technology may advance the field and provide reliable and valid measurements that are readily available to and manageable for providers, as well as acceptable, feasible, and sustainable for selected populations. RESULT AND CONCLUSIONS: Although new technologies must overcome many challenges, including privacy, efficiency, cost, and scalability, it is an exciting and fast-growing field with many potential applications and clinical benefit.

Behavioral intervention technologies: evidence review and recommendations for future research in mental health.

OBJECTIVE: A technical expert panel convened by the Agency for Healthcare Research and Quality and the National Institute of Mental Health was charged with reviewing the state of research on behavioral intervention technologies (BITs) in mental health and identifying the top research priorities. BITs refers to behavioral and psychological interventions that use information and communication technology features to address behavioral and mental health outcomes. METHOD: This study on the findings of the technical expert panel. RESULTS: Videoconferencing and standard telephone technologies to deliver psychotherapy have been well validated. Web-based interventions have shown efficacy across a broad range of mental health outcomes. Social media such as online support groups have produced disappointing outcomes when used alone. Mobile technologies have received limited attention for mental health outcomes. Virtual reality has shown good efficacy for anxiety and pediatric disorders. Serious gaming has received little work in mental health. CONCLUSION: Research focused on understanding reach, adherence, barriers and cost is recommended. Improvements in the collection, storage, analysis and visualization of big data will be required. New theoretical models and evaluation strategies will be required. Finally, for BITs to have a public health impact, research on implementation and application to prevention is required.

Electronic clinical decision support for management of depression in primary care: a prospective cohort study.

OBJECTIVE: To assess the utility of an electronic clinical decision support tool for management of depression in primary care. METHOD: This prospective study was conducted in a national network of ambulatory practices over a 1-year period (October 2007-October 2008). A clinical decision support tool was embedded into the electronic health record of 19 primary care practices with 119 providers. The main components included (1) the 9-item Patient Health Questionnaire (PHQ-9), with 9 questions paralleling the 9 DSM-IV criteria for the diagnosis of major depressive disorder; (2) a suicide assessment form; and (3) brief patient and provider education. Use of each component was tracked in the electronic health record. Providers completed baseline and postintervention surveys regarding their depression management practices and their perceptions of the clinical decision support tool. RESULTS: According to electronic health record tracking, the PHQ-9 form was used in 45.6% of the 16,052 adult patients with depression and in 73.7% of the 1,422 patients with new depression. The suicide assessment form was used in 62.0% of patients with possible suicidality. Education modules were rarely used. From before to after the study, providers reported increased use of standardized tools for depression diagnosis (47% to 80%, P < .001) and monitoring (27% to 85%, P < .001). The majority of providers reported often using the PHQ-9 and suicide forms and felt them to be very helpful in patient care, with 85% planning to continue their use after the study. CONCLUSIONS: The electronic health record-based clinical decision support tool was extensively used and perceived as very helpful for assessment of patients' symptoms but not for provider education. These findings can help guide national efforts incorporating clinical decision support for quality improvement.

Using electronic health record-based tools to screen for bipolar disorder in primary care patients with depression.

PURPOSE: National guidelines recommend screening all persons with depression for bipolar disorder (BPD); one way to facilitate screening is through the use of electronic health records (EHRs). This study examined the impact of an EHR-based screening and decision support tool on diagnosis and treatment of BPD among patients diagnosed with depression in primary care offices. METHODS: This nonrandomized, controlled trial was conducted in a national network of offices using EHRs. The intervention included a screening instrument and other tools for diagnosis and management of BPD, which were embedded into the EHR. This instrument automatically activated when a patient with a diagnosis of depression but no diagnosis of BPD was seen in the office. The primary outcomes were the rates of new diagnoses of BPD and prescription of new BPD medications during the 6-month study period (April to October 2009). RESULTS: Twenty-one offices with 75 clinicians and 8355 adult patients with depression composed the intervention group, whereas 17 offices with 81 clinicians and 8799 adult patients with depression served as the comparison group. The screening tool was used with 47.5% of intervention patients, of whom 2.5% scored at high or very high risk for BPD. Intervention patients were more likely than comparison patients to be newly diagnosed with BPD (1.11% vs. 0.36%; P < .01) and be prescribed new BPD medications (1.85% vs. 1.19%; P < .01). CONCLUSIONS: The study suggests that EHR-based tools can be useful for screening and management of BPD for patients with depression in primary care offices.

Changes in patients' beliefs about their antidepressant during the acute phase of depression treatment.

PURPOSE: To test hypotheses regarding medication beliefs in relation to adherence, side effects, and response during the acute phase of an antidepressant treatment episode. METHODS: Participants were 163 patients with unipolar major depression participating in the acute phase of a multi-stage trial of medication and psychotherapy. Before starting citalopram, patients underwent measures of treatment beliefs and depression. They continued taking citalopram until either responding, discontinuing due to side effects, or failing to respond within 14 weeks. Assessments of adherence, side effects and depression were made at weeks 2, 4, 6, 9, 12 and 14 (as applicable) and at trial exit. Beliefs were reevaluated at exit. RESULTS: Perceived need for medication increased between baseline and exit (P=.01) while perceived medication harmfulness dropped between baseline and exit (P<.0001). Adherence was related to baseline perceived need (P=.022), and side effects were related to baseline perceived harmfulness (P=.002). Change in depressive symptoms was significantly related to both baseline perceived need (P=.039) and mean adherence (P=.036) but not baseline perceived harmfulness (P=.184) or side effects (P=.102). At exit, perceived need was unrelated to change in depression severity (P=.565), while perceived harmfulness was related to prior side effects (P<.0001). CONCLUSION: Patients' medication perceptions become more pro-adherence as treatment proceeds. Their perceptions of antidepressant necessity predict their subsequent medication adherence and response, while their perceptions about medication harmfulness show strong prospective associations with actual side effects. Clinicians ought to adjust their prescribing decisions accordingly. Interventions that target beliefs ought to capitalize upon the apparently bidirectional association between harm perceptions and actual side effects.

Toward a more comprehensive assessment of depression remission: the Remission Evaluation and Mood Inventory Tool (REMIT).

OBJECTIVE: Depression remission continues to be defined in terms of resolution of the Diagnostic and Statistical Manual of Mental Disorders criteria symptoms. However, it may be useful to assess additional symptoms as part of a more complete evaluation of remission. We sought to develop an adjunct self-report measure that can be used with commonly used depression measures when assessing remission. METHODS: Secondary data analysis and expert input were used to develop candidate items that were evaluated cross-sectionally in 1003 primary care clinician-identified depressed patients from two practice-based research networks. Multivariable regression analysis, with self-assessed recovery as the dependent variable, identified five symptoms that contributed significantly beyond the Patient Health Questionnaire (PHQ)-8. Further analysis was performed in selected subsamples. RESULTS: Emotional control, contentedness, future seeming dark, ability to bounce back and happiness yielded an 11% increase in R(2) beyond 60% yielded by the PHQ-8. The summed Remission Evaluation and Mood Inventory Tool (REMIT) 5 items have a mean=9.6 (S.D.=4.5), range 0-20 and reliability of 0.86. Subsample analysis showed incremental R(2) ranging from 9% in men to 15% in African-Americans. CONCLUSION: Depression remission is a multidimensional concept that includes important nondepressive symptom dimensions. These important dimensions can be measured using a self-report instrument feasible for routine primary care. Pending longitudinal validation, REMIT5 is a promising tool for depression management.

Prospects for person-centred diagnosis in general medicine.

Fundamental changes in health care delivery are revealing the limitations of our collective focus on disease-specific and technology-driven health care. We increasingly treat diseases, not persons, and it moves individuals from active participants in the care process to passive recipients of interventions. This problem is especially important for general medicine, where we must maintain the balance between person and disease, caring and technology. In this chapter, we focus on prospects for person-centred diagnosis and treatment in general (primary care) medicine in this time of change. We describe one way to employ the biopsychosocial model to integrate person-centred diagnosis in routine clinical practice, and we propose a 'person-centred path' for primary care development with the health information technology tools we will need to develop to follow that path.

Long-term clinical outcomes of care management for chronically depressed primary care patients: a report from the depression in primary care project.

PURPOSE: Recent studies examining depression disease management report improvements in short-term outcomes, but less is known about whether improvements are sustainable over time. This study evaluated the sustained clinical effectiveness of low-intensity depression disease management in chronically depressed patients. METHODS: The Depression in Primary Care (DPC) intervention was introduced in 5 primary care practices in the University of Michigan Health System, with 5 matched practices selected as control sites. Clinicians were free to refer none, some, or all of their depressed patients at their discretion. Core clinical outcomes of remission and serial change in Patient Health Questionnaire (PHQ-8) scores for 728 DPC enrollees observed for up to 18 months after enrollment were compared with those for 78 patients receiving usual care who completed mailed questionnaires at baseline, 6, 12, and 18 months. RESULTS: DPC enrollees had sustained improvement in remission rates and reduced-function days over the full 18 months. Mean change in the PHQ-8 score over each 6-month interval was more favorable for DPC enrollees than for usual care patients, and the proportion of DPC enrollees in remission was higher at 6 months (43.4% vs 33.3%; P = .11), 12 months (52.0% vs 33.9%; P = .012), and 18 months (49.2% vs 27.3%; P = .004). Multivariate analysis controlling for age, sex, ethnicity, baseline severity, and comorbid medical illness confirmed that DPC enrollees had significantly more reduction in depressive symptom burden over 18 months. CONCLUSIONS: The DPC intervention produced sustained improvement in clinical outcomes over 18 months in a cohort of chronically depressed patients with persistent symptoms despite active treatment.