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A quality improvement project to increase cardiovascular disease screening in psychiatric patients

Dissertation
Author: Melissa Maki
Abstract:
Increased mortality among serious mentally ill (SMI) patients is largely due to cardiovascular disease (CVD). Primary health care for SMI patients is inadequate and poorly coordinated with mental health care. Given the life-threatening nature of the physical health risks associated with serious mental illness, psychiatric providers must be able to identify and monitor cardiovascular risk factors in their patients and to collaborate with primary care in their ongoing clinical management. In this project, a systematic approach to process improvement in CVD screening employed the FOCUS-PDCA model of quality improvement to clarify the current process of CVD screening at a community mental health center and to outline the steps to implementation and evaluation of a new, more effective process of identifying and managing CVD risk factors in the seriously mentally ill. To evaluate the extent of process improvement in CVD screening, patient records were reviewed at two points in time. The initial review gathered baseline data and then was repeated six months after an intervention that consisted of staff education and new monitoring tools.

Table of Contents Dedication ................................................................................................................................... iii Acknowledgements ..................................................................................................................... iv Abstract .........................................................................................................................................v Chapter I: Executive Summary .....................................................................................................1 Statement of the Problem ..................................................................................................1 Purpose of the Project .......................................................................................................3 Significance for Health Care Outcomes ...........................................................................3 Theoretical Rationale Guiding the Project ........................................................................4 Identification of Stakeholders ...........................................................................................4 Identification of Project Mentor ........................................................................................5 Summary ...........................................................................................................................6 Chapter II: Review of Literature ...................................................................................................7 Literature Related to Theoretical Rationale ......................................................................7 Literature Related to Project Outcomes ..........................................................................10 Summary .........................................................................................................................19 Chapter III: Implementation .......................................................................................................20 Participants ......................................................................................................................20 Design .............................................................................................................................20 Procedures .......................................................................................................................21 Measures .........................................................................................................................21 Budget .............................................................................................................................22 Timeline ..........................................................................................................................22

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Setting and Population ....................................................................................................23 Outcome Data Collection Process ..................................................................................25 Summary .........................................................................................................................25 Chapter IV: Project Findings ......................................................................................................26 Statistical Analysis ..........................................................................................................26 Descriptives.....................................................................................................................28 Findings Related to Expected Outcomes ........................................................................30 Summary .........................................................................................................................30 Chapter V: Project Summary ......................................................................................................32 Discussion of Findings ....................................................................................................32 Implications for Sustaining Systems Change .................................................................33 Conclusions .....................................................................................................................34 Dissemination of Results ................................................................................................34 Summary .........................................................................................................................35 References ...................................................................................................................................36 Appendixes: A. Cardiovascular Disease Screening Tool ....................................................................42 B. Sample Letter to Primary Care Provider ....................................................................44 C. Educational Materials Given to Participants ..............................................................46 D. Chart Review Tool .....................................................................................................53 E. CSS IRB Original Application ...................................................................................55 F. CSS IRB Approval with Corrections ..........................................................................60

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G. CSS IRB Application Revised to Reflect Changes ....................................................63 H. Logic Model ...............................................................................................................68

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List of Tables Table Page 1. FOCUS-PDCA Model Used to Improve CVD Screening Process ...................................8 2. Timeline Chart ................................................................................................................24 3. T-Test ..............................................................................................................................27 4. Descriptives.....................................................................................................................28 5. Evidence for Process Improvement ................................................................................29

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Chapter I: Executive Summary Mentally ill people die up to 25 years earlier than the general population (Parks, Svendsen, Singer, & Foti, 2006). Increased mortality among serious mentally ill (SMI) patients is largely due to physical illness. As in the general population, most deaths are caused directly by cardiovascular disease (CVD) and metabolic disorders including diabetes, hyperlipidemia, and obesity. The difference is that SMI patients have more risk factors for CVD than do those without mental illness. Primary health care for SMI patients is inadequate and poorly coordinated with mental health care. Many psychiatric providers are not in regular contact with primary care providers (PCPs). Given the life-threatening physical health risks associated with having a serious mental illness, psychiatric providers need to be aware of cardiovascular risk factors and how to assess for them. Communication between psychiatry and primary care needs to occur regularly to increase the quality and length of life for psychiatric patients. Statement of the Problem At the outset of this project, the Human Development Center (HDC) was a nonprofit mental health center that served a five-county area across two states. In 2009, the psychiatry department served approximately 2,500 patients. Previously, HDC lacked a mechanism to screen for CVD and communicate results to primary care. There was a tool to screen for metabolic syndrome, but it was unclear how often this tool was used or if results were communicated to PCPs, as would be appropriate. At the project’s start, HDC was utilizing a metabolic screening tool developed by its medical director, Peter Miller, M.D. This tool followed the metabolic screening recommendations of the American Heart Association (AHA). The screening tool was used only for those patients who were taking second-generation antipsychotic medication

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including clozapine, olanzapine, ziprasidone, risperdone, and aripiprazole. These medications were known to cause weight gain (Nasrallah et al., 2006). Metabolic syndrome was defined by the AHA as a combination of medical disorders that increased a person’s risk of developing CVD and diabetes mellitus (Grundy et al., 2005). It affected one in five people and the prevalence increased with age. According to the AHA, metabolic syndrome was diagnosed by the presence of three or more of the following signs: (a) blood pressure > 130/85; (b) triglycerides > 150 mg/dL; (c) fasting blood glucose > 100mg/dL, high density lipoprotein (HDL) <40mg/dL in men and <50mg/dL in women; and (d) waist circumference of > 40 inches in men and > 35 inches in women (Grundy et al., 2005). This project was premised on the belief that mental health providers needed to understand why their psychiatric patients were dying early and to screen for comorbid CVD in every adult patient. Thus, they needed to take vital signs, check laboratory values and waist circumference, discuss lifestyle choices, and complete in-depth medical histories with an emphasis on cardiovascular health. The existing tool at HDC did not prompt the clinician to inquire about family history of CVD and tobacco use or to measure body mass index (BMI). Most important, I believed that mental health providers needed to take the gathered information and communicate it to primary care so their patients could get appropriate treatment for the CVD that was killing them 25 years earlier than the general population (Parks et al., 2006). A review of patient records (n = 129) was conducted to gather baseline data related to CVD screening practices at HDC. An educational workshop was conducted for the psychiatric staff and a new CVD screening tool and sample letter for communicating with PCPs were introduced. Six months later, the charts were reviewed again (n = 117) to measure improvement.

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Purpose of the Project The purposes of this project were threefold: (a) to understand the current practices at HDC with respect to CVD screening and communication between mental health providers and PCPs; (b) to develop tools to improve the existing process of CVD screening; and (c) to educate mental health providers working on Assertive Community Treatment (ACT) teams—including psychiatrists, advanced practice registered nurses (APRNs), registered nurses (RNs), and case managers—about the importance of screening all patients for CVD. The overall goal of this project was to improve HDC’s processes of CVD screening and communication with PCPs. Significance for Health Care Outcomes With consistent use of a cardiovascular risk screening tool, psychiatric providers can quickly recognize CVD trends and routinely refer psychiatric patients to PCPs for further evaluation and treatment. Understanding a particular patient’s risk for CVD often guides a prescriber to different decisions about the use of second-generation antipsychotic medications. For example, knowing a patient has low HDL and high triglycerides and fasting blood glucose, is overweight, and has a family history of CVD should guide a prescriber to a medication that does not cause weight gain. In addition, awareness of a client’s cardiac risk factors stimulates discussion about lifestyle changes. Knowing that a smoker has a BMI in the obese range sets the stage for critically important conversations about smoking cessation, diet, exercise, previous attempts at lifestyle change, what promotes change, and what does not. Especially with the seriously mentally ill, discussions about cooking, shopping, and budgeting for food give the clinician insight into a patient’s level of functioning and the barriers to a healthier lifestyle. Bringing psychiatry together with primary care as a treatment team optimizes mental and physical health outcomes and reduces mortality in the mentally ill. In sum, a collaborative

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treatment team including patients, psychiatry, and primary care can address multiple aspects of health and enhance the important therapeutic alliances that optimize health outcomes (Vreeland & Edward, 2004). Theoretical Rationale Guiding the Project Creating change in a health care organization required a methodical, interdisciplinary approach. Research suggested that approximately 85% of all the problems encountered in health care were process problems (Merritt et al., 2003). A process was defined simply as a series of related steps that met patients’ needs. This meant that only the remaining 15% were equipment or employee performance problems (Merritt et al., 2003). The necessity of continuous quality improvement in health care was recognized by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO). In 1994, JCAHO’s standards placed significant emphasis upon improving organizational performance through analysis and systematic process improvement using the FOCUS-PDCA model (Redick, 1999). The acronym referred to the following steps: Find the opportunity, Organize a team, Clarify current knowledge of the process, Understand causes of process variation, Select the process to improve, Plan the improvement, Data collection, Check data for process improvement, and Act to continue improvement (Baker, 2002; Ramsey, Ormsby, & Marsh, 2000; Redick, 1999). Because this model supported a systematic approach to solving clinical process problems, it was applied to this project. Identification of Stakeholders Many people benefited from CVD screening and early intervention. Project stakeholders included the ACT teams in Duluth, Minnesota: D-ACT (Duluth Assertive Community Treatment) and T-ACT (Transitional Assertive Community Treatment). The ACT teams were

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multidisciplinary collaboratives with staff representatives from St. Louis County, the State of Minnesota, the Center for Alcohol and Drugs, Life House, and the Human Development Center. While both teams served adults with serious mental illness, the T-ACT team served younger adults, ages 18-25. The patients were in various stages of their illnesses; some were newly diagnosed, and some had been living with mental illness for many years. Most were on medical assistance (MA) and received either general assistance medical care (GAMC) or social security disability income (SSDI). Many had comorbid physical illness. Some did not have PCPs. The medical teams were composed of psychiatrists, APRNs, and RNs employed by HDC. Others who benefited from this project were HDC’s quality assurance committee, PCPs, and most important, patients and their families. Identification of Project Mentor My project mentor was Dr. Peter Miller. He had been a psychiatrist for almost 30 years and had been employed at HDC for almost 25 years. With his passion to deliver quality psychiatric care, Dr. Miller had been HDC’s medical director for 22 years. Dr. Miller had an in- depth understanding of both the business and treatment issues that impacted behavioral healthcare. He was influential at HDC and had the ability to facilitate staff buy-in and promote implementation of the screening tool. From the outset, he supported the project and took time to listen to my ideas about the importance of CVD screening to HDC’s patients and the benefits to the organization of process improvement in this area. He edited my CVD screening tool for inclusion of all the required elements. Most important, he demonstrated his investment in the project by using the tool every day, which communicated its importance to those who eventually followed his lead. Dr. Miller also referenced this project to others in staff meetings, quality assurance meetings, and reports to HDC’s Board of Directors.

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Summary CVD is the number one killer of Americans compared to the general population; people with SMI are at particularly high risk for early mortality related to CVD (Parks et al., 2006). It is challenging to make accurate assessments and manage the complex symptomatology of people with SMI, especially when complicated by medical comorbidity.

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Chapter II: Review of Literature Literature Related to Theoretical Rationale A process was defined as a series of related steps that met the customer’s needs using available staff and material resources. Along this series of steps, staff provided inputs to the process while outputs were created for the patients. To improve a process, this patient-staff relationship had to be understood by all the staff involved in the performance improvement effort (Merritt et al., 2003). Performance improvement evaluated the processes contributing to patient care by tracking their functions, stability, outcomes, and opportunities for improvement (Dianis & Cummings, 1998). The FOCUS-PDCA model was a conceptual framework created by the Hospital Corporation of America (HCA) for thinking about and applying different tools for continuous quality improvement. JCAHO standards placed significant emphasis upon improving organizational performance through the analysis and systematic improvement of process (Redick, 1999). Each letter of the model represented a step in the quality improvement process (see Table 1).

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Table 1

FOCUS - PDCA Model Used to Improve CVD Screening Process

F ind the opportunity

Mentally i ll people dying early

Stakeholders identified

Fits with agency’s mission/vision

O rganize the team who knows the pr ocess

Medical staff

Other key support staff and case managers

C larify current knowledge of the process

Research CVD in psychiatric patients

Initial chart review to understand current CVD monitoring processes

U nderstand the causes of process var iation

Identify barriers to CVD screening and communicating with PCPs

S elect the process improvement

Mechanism for quality improvement understood by all involved

Educational workshop to outline problem, share chart review, introduce improvement to ols

P lan the improvement

Obtain necessary supplies and forms, e.g., BP cuffs, tape measure, scales

D o the improvement

Data collection for a six - month period

Monthly check - in with teams to monitor progress and answer questions

C heck the results

Second chart review six

months post - educational workshop

Identify barriers to change

A ct to hold the gain

Disseminate findings

Implement protocol agency wide

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Elements of this project’s methodology can illustrate application of the conceptual model. First, one had to find opportunity. For purposes of this project, the opportunity was that mentally ill people were dying early (Parks et al., 2006). In this step, stakeholders were identified, and the agency’s mission and vision statements were recognized as a fit for the project. Organizing the team who knew the process meant including not only the medical staff working with the D-ACT and T-ACT teams but also the support staff who ensured that materials were available to the clinicians. It also meant including the case managers who reminded clients to get the necessary laboratory work and who offered them transportation. In the clarifying step, an initial chart review was conducted to gather baseline data about how often elements of CVD screening were documented. In order to understand the causes of process variation, potential barriers to the study were identified including (a) insurance limitations, (b) treatment nonadherence, and (c) mental health symptoms. The next step selected the process improvement. In this step, an educational workshop was presented to ensure that all participants understood the quality improvement project. Planning the improvement was very important. Staff needed to have the necessary supplies and resources to provide the service to patients. The next phase involved implementing the improvement. Clinicians used the new CVD screening tool for a period of six months. After six months, results were checked and barriers to quality improvement were identified. The last stage in the FOCUS-PDCA model addressed the need to act to sustain changes. Once the study was over, dissemination of findings and agency-wide implementation of the CVD screening tool would serve to sustain the process improvement. While the FOCUS-PDCA model has been reported to improve a variety of clinical processes, few published studies demonstrated its utility in health care. In one such study, Manfredi, Canziani, Draibe, and Dalboni (2003) described continuous surveillance of the quality

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of dialysis through systematic monitoring of indicators of morbidity and mortality. Investigators tracked the hepatitis B virus and discovered that transmission occurred due to the breakdown of barriers during the dialysis procedure. FOCUS-PDCA concepts permitted immediate identification of a hepatitis B outbreak. Once identified, the virus was contained. Literature Related to Project Outcomes Each year more than 33 million Americans seek mental health care for psychiatric or substance abuse problems (Institute of Medicine [IOM], 2006). This number has an enormous impact on the health care system of the United States. The National Association of State Mental Health Program Directors reported that people with SMI were dying from causes similar to those found in the general population (Parks et al., 2006), but their standardized mortality rates were two to three times higher than those without mental illness. Failure to adequately assess, prevent, and manage physical health had significant effects on the day-to-day functioning, quality of life, health care costs, and life spans of the severely mentally ill. Unfortunately, the physical health of psychiatric patients proved to be the biggest challenge, for they often got either suboptimal or no primary health care (Dombrovski & Rosenstock, 2004). Secondary prevention, which involved the early identification and treatment of an illness, could be initiated through routine screening of every adult patient (Carney, Yates, Goerdt, & Doebbeling, 1998). Mortality. Mortality rates have been used as global measures of a population’s health status in order to determine public health efforts and medical treatments (Colton & Manderscheid, 2006). On average, people with serious mental illness have been dying 25 years earlier than the general population (Parks et al., 2006). Several studies have looked at state death rates of psychiatric patients. First, Colton and Manderscheid (2006) compared the mortality of public mental health clients in eight states with the mortality of their states’ corresponding

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general population. In all eight states, mentally ill clients died much earlier than those who were not mentally ill. Furthermore, most deaths had natural causes, similar to the leading causes of death found nationwide. Second, researchers from the Ohio Department of Health analyzed death records from 1998-2002. The mean age of death was 47.7 years, amounting to 32 years of life lost per patient (Parks et al., 2006). Drembling, Chen, and Vachon (1999) reported on mortality rates of the mentally ill in Massachusetts from 1989-1994. The authors analyzed 1,890 deaths of psychiatric patients and found that these patients lost 8.8 years of potential life compared to the general population. Men had a higher average of 14.1 years of potential life lost than women, who lost 5.7 years. Third, a retrospective Canadian study found a 20% shorter life expectancy for schizophrenics (Newman & Bland, 1991). Finally, in the last three decades, more than 50 published studies supported the claim that depression caused early death. A meta-analysis of 57 studies found that 51% supported the link between mortality and depression, 23% did not support it, and 26% had mixed results (Wulsin, Valliant, & Wells, 1999). Although there were few controlled, well designed studies that provided incontrovertible evidence, it was clear that at least some depressed people had a four-fold increase in death compared to control groups (Wulsin et al., 1999). Cardiovascular disease. While suicide and injury make up 30-40% of excess mortality, 60% of premature deaths in schizophrenics were due to medical conditions like cardiovascular, pulmonary, and infectious diseases (Parks et al., 2006). Worldwide, heart disease was the leading cause of death, disability, and health care costs. The World Health Organization (WHO) estimated that 14.7 million people died of CVD in 1990, which increased to 17 million in 1999 (Bonow, Smaha, Smith, Mensah, & Lenfant, 2002). Mentally ill patients were at higher risk for developing CVD than the rest of the U.S. population. Increased mortality among SMI patients

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was directly related to CVD and metabolic disorders including diabetes, hyperlipidemia, and obesity. These statistics became more important as second generation antipsychotics were more widely prescribed (Dombrovski & Rosenstock, 2004). Berren, Hill, Merikle, Gonzalez, and Santiago (1994) found that serious mentally ill people were more than twice as likely than others to die of cardiovascular causes. Startling results emerged from the Clinical Atypical Trial Intervention Evaluation (CATIE) study. At baseline, investigators found that 47% of participants had dyslipidemia, 33% had hypertension, and 11% had diabetes. Of those with dyslipidemia, 88% were not being treated; of those with hypertension, 66% were not being treated; and of those with diabetes, 30% were not being treated (Nasrallah et al., 2006). Risk factors. High prevalence of cardiovascular risk factors may account for the increased rate of cardiovascular mortality among the mentally ill (Davidson et al., 2001). According to the WHO (2003), CVD encompassed coronary heart disease, stroke, hypertension, inflammatory heart disease, rheumatic heart disease, and congenital heart disease. Several studies supported evidence in which CVD was twice as likely in schizophrenic patients than in the general population (Allebeck, 1989; Dombrovski & Rosenstock, 2004). In a retrospective study, Miller, Paschall, and Svendsen (2006) found that obesity, hypertension, diabetes, and COPD were the most prevalent medical comorbidities, which was consistent with other research in this area. Many factors explained why mentally ill people were more prone to CVD than the general public. Medication-induced weight gain, decreased physical activity, increased rates of smoking, and inadequate social support systems were all likely contributors to heart disease, hypertension, diabetes, and respiratory disorders (Miller et al., 2006). Depression increased the

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risk of CVD through both direct effect and poor self-care (Lawson, Vaillant, & Wells, 1999). Issues that compounded CVD included (a) access to care, (b) quality of care, (c) aging of the population, (d) increased prevalence of obesity, (e) hypertension, (f) hypercholesterolemia, (g) diabetes, (h) smoking, and (i) lack of physical activity (Bonow et al., 2002). CVD risks were categorized as non-modifiable risk factors, emerging risk factors, and modifiable risk factors (Bermudes, 2007). Non-modifiable risk factors included (a) gender, (b) family history, and (c) age. Emerging risk factors included (a) lipoprotein A, (b) homocysteine, (c) prothrombotic factors, (d) proinflammatory factors, (e) impaired fasting glucose, and (f) subclinical atherosclerosis. Modifiable risk factors included (a) obesity, (b) smoking, (c) diabetes, (d) hypertension, and (e) dyslipidemia. This project focused on modifiable CVD risk factors. It was important to remember that CVD risk factors were additive and that the total CVD risk of a person increased with each additional risk factor (Bermudes, 2007). Obesity. WHO (2003) estimated that one billion people worldwide were overweight or obese. Results from the 2003-2004 National Health and Nutritional Survey (NHANES) indicated that an estimated 66% of U.S. adults were either overweight or obese. According to the National Heart, Lung, and Blood Institute’s Institute on Obesity Education Initiative (NHLBI, 2008), obesity has reached epidemic proportions and is an independent risk factor for CVD. Obesity is known to influence the impact of heart disease by increasing risk for hypertension, diabetes, hypertriglyceridemia, low HDL cholesterol, and high LDL cholesterol (National Heart Lung and Blood Institute [NHLBI], 2008). One of the Healthy People 2010 objectives was to decrease obesity to less than 15% (Centers for Disease Control and Prevention [CDC], 2008).

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People with serious mental illness have a higher prevalence of obesity (Vreeland & Edward, 2004). This may be due to psychiatric medication. Many psychotropic medications including antipsychotics, antidepressants, and mood stabilizers have been associated with weight gain (Vreeland & Edward, 2004). Elmslie, Mann, Silverstone, Williams, and Romans (2001) conducted a cross-sectional study of the nutrient intake and physical activity of 89 patients with bipolar disorder, confirming that drug-induced changes in food cravings led to a high intake of sucrose. Parks et al. (2006) found that 26% of bipolar patients were obese compared to 45-55% of schizophrenic patients. Treatment of obesity was a two-step process involving assessment and treatment, then management (NHLBI, 2008). Assessment included (a) measuring BMI, (b) diet, (c) waist circumference, and (d) level of motivation. Health care providers could address weight concerns by first accurately assessing for obesity. Assessing BMI was one of the easiest, most accurate ways to determine whether an adult was overweight or obese (Vreeland & Edward, 2004). BMI was calculated by dividing a person’s weight by his or her height. BMI categories were underweight = <18.5, normal weight = 18.5-24.9, overweight = 25-29.9, and obese = 30 or greater (NHLBI, 2008). Monitoring weights was recommended for all psychiatric patients. BMI should be checked before starting medications, at medication changes, at every visit for six months, and then quarterly once weight was stable (Marder, Essock, & Miller, 2004). The NHLBI’s guidelines for treatment strategies (2008) included modifications in diet and increased physical activity. Being overweight or obese was associated with an increased risk for developing CVD. These effects were seen consistently, which underlined the importance of primary prevention and treatment of excess weight (Wilson, D’Agostino, Sullivan, Parise, & Kannel, 2002).

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Hypertension. The prevalence of hypertension was more than 10% in 19 countries (WHO, 2003). Hypertension contributed to the prevalence of stroke, coronary heart disease, heart failure, and renal failure (WHO, 2003). Parks et al. (2006) reported an increased prevalence of hypertension in people with schizophrenia and bipolar disorder compared to the general population. Schizophrenic patients had high blood pressure at a rate of more than 18%, and the prevalence of high blood pressure in bipolar patients was 15%. Of the 1,448 participants in the CATIE study, 33.2% had hypertension (Nasrallah et al., 2006). Hypertension was defined as a systolic threshold of 140mm Hg (WHO, 2003). The WHO International Society of Hypertension reported that treating hypertension yielded a 40% reduction in the risk of stroke and a 15% reduction in the risk of heart attack. Dyslipidemia. Elevated cholesterol and triglyceride levels were associated with CVD including ischemic heart disease and myocardial infarction (Jeppesen, Heiin, Suadicani, & Gyntelberg, 1998). A 10% increase in cholesterol level was associated with a 20-30% higher risk of developing CVD, and lowering cholesterol by 10% could decrease the chances of developing CVD by 20-30% (LaRosa et al. 1990). The relative risk of dyslipidemia for schizophrenic patients was up to five times that of the general population (Parks et al., 2006). Nasrallah et al. (2006) reported that the prevalence of dyslipidemia was much greater than that of the control group at the start of the CATIE trial. Specifically, 47.3% of participants had elevated triglycerides and 43.3% had low levels of HDL cholesterol. Despite their high risk for CVD, patients with serious mental illness were less likely than others to receive treatment for dyslipidemia. At the start of the CATIE trial, only 12% of the participants with dyslipidemia were taking medications to regulate their lipids (Nasrallah et al., 2006).

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Diabetes. Brown, Inskip, and Barraclough (2000) studied 79 psychiatric patient deaths, 21 of which could have been avoided. Fourteen died from CVD, three from diabetes, and five from lung cancer. According to the Parks et al. (2006), seriously mentally ill patients were 10- 14% more likely to have diabetes compared to the general population. Patients at significant risk for diabetes mellitus had a family history of diabetes, BMI of 25 or greater, and waist circumference 35 inches or more for women, and 40 inches or more for men (Marder et al., 2004). Patients should have a baseline plasma glucose level before a new antipsychotic medication is initiated. If a fasting glucose is not possible, a hemoglobin A1c is in order. Patients should have a fasting glucose or a hemoglobin A1c four months after starting a second generation antipsychotic, then yearly. If gaining weight, they should have labs ordered every four months (Marder et al., 2004). Smoking. Smoking had tremendous effects on the cardiovascular system including an increased risk for heart attack, stroke, and death (Bermudes, 2007). Mortality from CVD and stroke was 2-3 times higher among smokers than nonsmokers (Bonow et al., 2002; Goff, Sullivan, & McEvoy, 2005). While there has been an overall reduction in U.S. smoking rates generally, smoking was still highly prevalent in the mentally ill population (Lasser, Boyd, & Wollhandler, 2000). Lasser et al. (2000) found that people with mental illness were twice as likely to smoke and made up as much as half of the tobacco consumers in the U.S. Schizophrenic patients were twice as likely to smoke than patients in control groups (Goff et al., 2005). Hughes, Hatsukami, Mitchell, and Dahlgren (1986) found that the prevalence of smoking also varied by the severity of illness. Sixty-two percent of those who had been hospitalized were smokers compared to 38% of those who had never been hospitalized. In addition, 61% of those who were taking psychotropic medications smoked compared to 41% of those who were not prescribed

Full document contains 79 pages
Abstract: Increased mortality among serious mentally ill (SMI) patients is largely due to cardiovascular disease (CVD). Primary health care for SMI patients is inadequate and poorly coordinated with mental health care. Given the life-threatening nature of the physical health risks associated with serious mental illness, psychiatric providers must be able to identify and monitor cardiovascular risk factors in their patients and to collaborate with primary care in their ongoing clinical management. In this project, a systematic approach to process improvement in CVD screening employed the FOCUS-PDCA model of quality improvement to clarify the current process of CVD screening at a community mental health center and to outline the steps to implementation and evaluation of a new, more effective process of identifying and managing CVD risk factors in the seriously mentally ill. To evaluate the extent of process improvement in CVD screening, patient records were reviewed at two points in time. The initial review gathered baseline data and then was repeated six months after an intervention that consisted of staff education and new monitoring tools.