Clinical and economic impact of implementing a comprehensive diabetes management program in managed care

A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma

In the United States, ~9% of people have asthma. Each year, asthma incurs high health care cost and many hospital encounters covering 1.8 million emergency room visits and 439,000 hospitalizations. A small percentage of patients with asthma use most health care resources. To improve outcomes and cut resource use, many health care systems use predictive models to prospectively find high-risk patients and enroll them in care management for preventive care. For maximal benefit from costly care management with limited service capacity, only patients at the highest risk should be enrolled. However, prior models built by others miss >50% of true highest-risk patients and mislabel many low-risk patients as high risk, leading to suboptimal care and wasted resources. To address this issue, 3 site-specific models were recently built to predict hospital encounters for asthma, gaining up to >11% better performance. However, these models do not generalize well across sites and patient subgroups, creating 2 gaps before translating these models into clinical use. This paper points out these 2 gaps and outlines 2 corresponding solutions: (1) a new machine learning technique to create cross-site generalizable predictive models to accurately find high-risk patients and (2) a new machine learning technique to automatically raise model performance for poorly performing subgroups while maintaining model performance on other subgroups. This gives a roadmap for future research.

Using a Constraint-Based Method to Identify Chronic Disease Patients Who Are Apt to Obtain Care Mostly Within a Given Health Care System: Retrospective Cohort Study

BACKGROUND

For several major chronic diseases including asthma, chronic obstructive pulmonary disease, chronic kidney disease, and diabetes, a state-of-the-art method to avert poor outcomes is to use predictive models to identify future high-cost patients for preemptive care management interventions. Frequently, an American patient obtains care from multiple health care systems, each managed by a distinct institution. As the patient's medical data are spread across these health care systems, none has complete medical data for the patient. The task of building models to predict an individual patient's cost is currently thought to be impractical with incomplete data, which limits the use of care management to improve outcomes. Recently, we developed a constraint-based method to identify patients who are apt to obtain care mostly within a given health care system. Our method was shown to work well for the cohort of all adult patients at the University of Washington Medicine for a 6-month follow-up period. It is unknown how well our method works for patients with various chronic diseases and over follow-up periods of different lengths, and subsequently, whether it is reasonable to perform this predictive modeling task on the subset of patients pinpointed by our method.

OBJECTIVE

To understand our method's potential to enable this predictive modeling task on incomplete medical data, this study assesses our method's performance at the University of Washington Medicine on 5 subgroups of adult patients with major chronic diseases and over follow-up periods of 2 different lengths.

METHODS

We used University of Washington Medicine data for all adult patients who obtained care at the University of Washington Medicine in 2018 and PreManage data containing usage information from all hospitals in Washington state in 2019. We evaluated our method's performance over the follow-up periods of 6 months and 12 months on 5 patient subgroups separately-asthma, chronic kidney disease, type 1 diabetes, type 2 diabetes, and chronic obstructive pulmonary disease.

RESULTS

Our method identified 21.81% (3194/14,644) of University of Washington Medicine adult patients with asthma. Around 66.75% (797/1194) and 67.13% (1997/2975) of their emergency department visits and inpatient stays took place within the University of Washington Medicine system in the subsequent 6 months and in the subsequent 12 months, respectively, approximately double the corresponding percentage for all University of Washington Medicine adult patients with asthma. The performance for adult patients with chronic kidney disease, adult patients with chronic obstructive pulmonary disease, adult patients with type 1 diabetes, and adult patients with type 2 diabetes was reasonably similar to that for adult patients with asthma.

CONCLUSIONS

For each of the 5 chronic diseases most relevant to care management, our method can pinpoint a reasonably large subset of patients who are apt to obtain care mostly within the University of Washington Medicine system. This opens the door to building models to predict an individual patient's cost on incomplete data, which was formerly deemed impractical.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/13783.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Does delay in planned diabetes care influence outcomes for aboriginal Australians? A study of quality in health care

BACKGROUND

To examine the association between delay in planned diabetes care and quality of outcomes.

METHODS

A retrospective analysis of primary care and inpatient records for 2567 Aboriginal patients, with diabetes, living in 49 remote communities in the Northern Territory of Australia. Poisson regression was used to estimate the association between delay from diagnosis to documented diabetes care plan and three outcome measures: mean HbA1c level, most recent blood pressure and number of diabetes-related hospital admissions.

RESULTS

Compared with no delay (< 60 days), patients with delay had increased risk of elevated mean HbA1c: 60 days to < 2 years, incidence rate ratio (IRR), 1.2 (95% CI:1.07-1.39); 2 years to < 4 years, incidence rate ratio (IRR), 1.2 (95% CI:1.04-1.45); 4 years and over, incidence rate ratio (IRR), 1.3 (95% CI:1.12-1.52). There was no evidence of association between delay and optimal blood pressure control. Risk of diabetes-related admission increased with increased delay. Compared with no delay the IRRs for delay were: 60 days to < 2 years, 1.2 (95% CI:1.07-1.42); 2 to < 4 years, 1.3 (95% CI: 1.15-1.58): and 4 years and over, 2.6 (95% CI,2.28-3.08).

CONCLUSION

The study found that a timely diabetes care plan was associated with better short-term blood glucose control and fewer diabetes-related admissions but not with improved blood pressure control. Delays may be a result of both patient and service-related factors.

Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis

Background

Both chronic obstructive pulmonary disease (COPD) and asthma incur heavy health care burdens. To support tailored preventive care for these 2 diseases, predictive modeling is widely used to give warnings and to identify patients for care management. However, 3 gaps exist in current modeling methods owing to rarely factoring in temporal aspects showing trends and early health change: (1) existing models seldom use temporal features and often give late warnings, making care reactive. A health risk is often found at a relatively late stage of declining health, when the risk of a poor outcome is high and resolving the issue is difficult and costly. A typical model predicts patient outcomes in the next 12 months. This often does not warn early enough. If a patient will actually be hospitalized for COPD next week, intervening now could be too late to avoid the hospitalization. If temporal features were used, this patient could potentially be identified a few weeks earlier to institute preventive therapy; (2) existing models often miss many temporal features with high predictive power and have low accuracy. This makes care management enroll many patients not needing it and overlook over half of the patients needing it the most; (3) existing models often give no information on why a patient is at high risk nor about possible interventions to mitigate risk, causing busy care managers to spend more time reviewing charts and to miss suited interventions. Typical automatic explanation methods cannot handle longitudinal attributes and fully address these issues.

Objective

To fill these gaps so that more COPD and asthma patients will receive more appropriate and timely care, we will develop comprehensible data-driven methods to provide accurate early warnings of poor outcomes and to suggest tailored interventions, making care more proactive, efficient, and effective.

Methods

By conducting a secondary data analysis and surveys, the study will: (1) use temporal features to provide accurate early warnings of poor outcomes and assess the potential impact on prediction accuracy, risk warning timeliness, and outcomes; (2) automatically identify actionable temporal risk factors for each patient at high risk for future hospital use and assess the impact on prediction accuracy and outcomes; and (3) assess the impact of actionable information on clinicians’ acceptance of early warnings and on perceived care plan quality.

Results

We are obtaining clinical and administrative datasets from 3 leading health care systems’ enterprise data warehouses. We plan to start data analysis in 2020 and finish our study in 2025.

Conclusions

Techniques to be developed in this study can boost risk warning timeliness, model accuracy, and generalizability; improve patient finding for preventive care; help form tailored care plans; advance machine learning for many clinical applications; and be generalized for many other chronic diseases.

International Registered Report Identifier (IRRID)

PRR1-10.2196/13783

A Roadmap for Optimizing Asthma Care Management via Computational Approaches

Asthma affects 9% of Americans and incurs US $56 billion in cost, 439,000 hospitalizations, and 1.8 million emergency room visits annually. A small fraction of asthma patients with high vulnerabilities, severe disease, or great barriers to care consume most health care costs and resources. An effective approach is urgently needed to identify high-risk patients and intervene to improve outcomes and to reduce costs and resource use. Care management is widely used to implement tailored care plans for this purpose, but it is expensive and has limited service capacity. To maximize benefit, we should enroll only patients anticipated to have the highest costs or worst prognosis. Effective care management requires correctly identifying high-risk patients, but current patient identification approaches have major limitations. This paper pinpoints these limitations and outlines multiple machine learning techniques to address them, providing a roadmap for future research.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

Background

To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient’s weight kept rising in the past year). This process becomes infeasible with limited budgets.

Objective

This study’s goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data.

Methods

This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes.

Results

We are currently writing Auto-ML’s design document. We intend to finish our study by around the year 2022.

Conclusions

Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes.

An Innovative Approach to Health Care Delivery for Patients with Chronic Conditions

Although the health care reform movement has brought about positive changes, lingering inefficiencies and communication gaps continue to hamper system-wide progress toward achieving the overarching goal—higher quality health care and improved population health outcomes at a lower cost. The multiple interrelated barriers to improvement are most evident in care for the population of patients with multiple chronic conditions. During transitions of care, the lack of integration among various silos and inadequate communication among providers cause delays in delivering appropriate health care services to these vulnerable patients and their caregivers, diminishing positive health outcomes and driving costs ever higher. Long-entrenched acute care-focused treatment and reimbursement paradigms hamper more effective deployment of existing resources to improve the ongoing care of these patients. New models for care coordination during transitions, longitudinal high-risk care management, and unplanned acute episodic care have been conceived and piloted with promising results. Utilizing existing resources, Mobile Integrated Healthcare is an emerging model focused on closing these care gaps by means of a round-the-clock, technologically sophisticated, physician-led interprofessional team to manage care transitions and chronic care services on-site in patients' homes or workplaces.

PredicT-ML: a tool for automating machine learning model building with big clinical data

Background

Predictive modeling is fundamental to transforming large clinical data sets, or “big clinical data,” into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs.

Methods

This paper describes our design of and vision for PredicT-ML (prediction tool using machine learning), a software system that aims to overcome these barriers and automate machine learning model building with big clinical data.

Results

The paper presents the detailed design of PredicT-ML.

Conclusions

PredicT-ML will open the use of big clinical data to thousands of healthcare administrators and researchers and increase the ability to advance clinical research and improve healthcare.

Primary care referral to multidisciplinary high risk foot services - too few, too late

Background

To determine if patients with no contact with a multi-disciplinary team High Risk Foot Service (MDT-HRFS) had an increased rate of hospital admission for diabetes foot infection compared to patients with contact. Secondary aims were to report on clinical outcomes.

Methods

A retrospective study was conducted at a major tertiary referral hospital in metropolitan Sydney over 12 months. An ICD-10 search of the electronic medical record system and paper medical charts identified patients with diabetes mellitus (type 1 or type 2) and a primary admission for diabetes foot infection (DFI). Patients were categorised as having contact or no contact with an MDT-HRFS.

Results

One hundred ninety-six hospital admissions (156 patients) were identified with DFI over a 12-month period. Patients with no contact with a MDT-HRFS represented three quarters of admissions (no contact = 116, 74.7 % vs. contact = 40, 25.6 %, p = 0.0001) and presented with more severe infection (no contact = 39 vs. contact = 12). The odds of lower extremity amputation increased five-fold when both no contact and severe infection occurred in combination (no contact with severe infection and amputation = 34, 82.9 % vs. contact with severe infection and amputation = 7, 17.1 %, OR 4.9, 95 % CI 1.1–21.4, p = 0.037). Using estimates of both the contact and no contact population of people with diabetes and high-risk feet and assuming the risk of amputation was the same, than the number of expected amputations in the no contact group should have been 55, however the observed number was 77, 22 more than expected (p = 0.0001).

Conclusions

Patients with no contact with a MDT-HRFS represented the majority of admissions for DFIs to a tertiary referral hospital. This group on population estimates appears to be at high risk of amputation of the lower extremity and therefore early referral of this high-risk group might lower this risk.

Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods

Background

Chronic diseases affect 52% of Americans and consume 86% of health care costs. A small portion of patients consume most health care resources and costs. More intensive patient management strategies, such as case management, are usually more effective at improving health outcomes, but are also more expensive. To use limited resources efficiently, risk stratification is commonly used in managing patients with chronic diseases, such as asthma, chronic obstructive pulmonary disease, diabetes, and heart disease. Patients are stratified based on predicted risk with patients at higher risk given more intensive care. The current risk-stratified patient management approach has 3 limitations resulting in many patients not receiving the most appropriate care, unnecessarily increased costs, and suboptimal health outcomes. First, using predictive models for health outcomes and costs is currently the best method for forecasting individual patient’s risk. Yet, accuracy of predictive models remains poor causing many patients to be misstratified. If an existing model were used to identify candidate patients for case management, enrollment would miss more than half of those who would benefit most, but include others unlikely to benefit, wasting limited resources. Existing models have been developed under the assumption that patient characteristics primarily influence outcomes and costs, leaving physician characteristics out of the models. In reality, both characteristics have an impact. Second, existing models usually give neither an explanation why a particular patient is predicted to be at high risk nor suggestions on interventions tailored to the patient’s specific case. As a result, many high-risk patients miss some suitable interventions. Third, thresholds for risk strata are suboptimal and determined heuristically with no quality guarantee.

Objective

The purpose of this study is to improve risk-stratified patient management so that more patients will receive the most appropriate care.

Methods

This study will (1) combine patient, physician profile, and environmental variable features to improve prediction accuracy of individual patient health outcomes and costs; (2) develop the first algorithm to explain prediction results and suggest tailored interventions; (3) develop the first algorithm to compute optimal thresholds for risk strata; and (4) conduct simulations to estimate outcomes of risk-stratified patient management for various configurations. The proposed techniques will be demonstrated on a test case of asthma patients.

Results

We are currently in the process of extracting clinical and administrative data from an integrated health care system’s enterprise data warehouse. We plan to complete this study in approximately 5 years.

Conclusions

Methods developed in this study will help transform risk-stratified patient management for better clinical outcomes, higher patient satisfaction and quality of life, reduced health care use, and lower costs.

Diabetes care among elderly medicare beneficiaries with Parkinson's disease and diabetes

Background

Elderly individuals with type 2 diabetes mellitus (T2DM) suffer from several comorbidities, which affect their health outcomes, as well as process of care. This study assessed process and intermediate clinical outcomes of diabetes care among elderly individuals with T2DM and co-occurring Parkinson’s disease(PD).

Methods

This study used a retrospective cohort design with propensity score matching using Humana Medicare Advantage Part D claims database (2007-2011) and included elderly (age ≥ 65 years) Medicare beneficiaries with T2DM (identified by ICD-9-CM code of 250.x0 or 250.x2). PD was identified using ICD-9-CM code of 332.xx. After propensity score matching there were 2,703 individuals with T2DM and PD and 8,109 with T2DM and no PD. The three processes of care measures used in this study included: (i) HbA1c test; (ii) Lipid test; (iii) and Nephropathy screening. Intermediate clinical outcomes consisted of glycemic and lipid control.

Results

Multivariable conditional logistic regressions revealed that elderly individuals with T2DM and PD were 12 % (AOR: 0.88, 95 %CI: 0.79-0.97) and 18 % (AOR: 0.82, 95 %CI: 0.72-0.94) less likely to meet the annual American Diabetes Association (ADA) recommended HbA1c and lipid testing goals respectively compared to individuals with T2DM and no PD. Multinomial conditional logistic regressions showed that elderly individuals with T2DM and PD were more likely to have HbA1c and lipid (HbA1c < 8 %; LDL-C <100 mg/dl; HDL-C ≥ 50 mg/dl; triglyceride <150 mg/dl; and total cholesterol <200 mg/dl) control.

Conclusions

Among elderly individuals with T2DM, those with PD were less likely to achieve ADA recommended annual HbA1c and lipid testing compared to those without PD. However, PD individuals were more likely to achieve intermediate glycemic and lipid control.

Expanding Paramedicine in the Community (EPIC): study protocol for a randomized controlled trial

BACKGROUND

The incidence of chronic diseases, including diabetes mellitus (DM), heart failure (HF) and chronic obstructive pulmonary disease (COPD) is on the rise. The existing health care system must evolve to meet the growing needs of patients with these chronic diseases and reduce the strain on both acute care and hospital-based health care resources. Paramedics are an allied health care resource consisting of highly-trained practitioners who are comfortable working independently and in collaboration with other resources in the out-of-hospital setting. Expanding the paramedic's scope of practice to include community-based care may decrease the utilization of acute care and hospital-based health care resources by patients with chronic disease.

METHODS/DESIGN

This will be a pragmatic, randomized controlled trial comparing a community paramedic intervention to standard of care for patients with one of three chronic diseases. The objective of the trial is to determine whether community paramedics conducting regular home visits, including health assessments and evidence-based treatments, in partnership with primary care physicians and other community based resources, will decrease the rate of hospitalization and emergency department use for patients with DM, HF and COPD. The primary outcome measure will be the rate of hospitalization at one year. Secondary outcomes will include measures of health system utilization, overall health status, and cost-effectiveness of the intervention over the same time period. Outcome measures will be assessed using both Poisson regression and negative binomial regression analyses to assess the primary outcome.

DISCUSSION

The results of this study will be used to inform decisions around the implementation of community paramedic programs. If successful in preventing hospitalizations, it has the ability to be scaled up to other regions, both nationally and internationally. The methods described in this paper will serve as a basis for future work related to this study.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT02034045. Date: 9 January 2014.

Diabetes education: quality improvement interventions through health departments

BACKGROUND

As the burden of diabetes continues to overwhelm the public health system, there is increased demand on local health departments (LHDs) to improve public health services. Quality improvement (QI) techniques have been shown to be an effective means to improve the delivery of services by LHDs.

PURPOSE

To evaluate the extent to which the adoption of organizational QI strategies influences the delivery and outreach of diabetes self-management education (DSME) services provided by LHDs.

METHODS

A change facilitation model that included QI team development and on-site QI training and facilitation was delivered to six LHDs that provide DSME, during 2010-2011. After training, each LHD developed and implemented a QI project to improve the outreach and delivery of DSME services. Pre- and post-intervention surveys were administered to evaluate the extent of change in DSME outreach and delivery. Data were analyzed in 2011.

RESULTS

The number of individuals who completed an entire course of DSME increased by >100%, and 14% more diabetics attended DSME on a monthly basis. Half of LHDs reported receiving increased numbers of referrals per month, and 15% more healthcare providers referred diabetic patients to the LHD for DSME.

CONCLUSIONS

Participation in Community Outreach and Change for Diabetes Management (COACH 4DM) led to improvements in the LHD QI infrastructure, and in the outreach and delivery of services to diabetic patients. The techniques used during COACH 4DM are applicable to a wide variety of contexts and may be an effective tool to improve the delivery of other clinical and community preventive services.

Value of team approach combined with clinical pathway for diabetic foot problems: a clinical evaluation

AIMS

To evaluate the effectiveness of management of diabetic foot problems (DFP) by the National University Hospital (NUH) Multidisciplinary Diabetic Foot Team combined with a clinical pathway in terms of average length of stay (ALOS), readmission rates, hospitalisation cost per patient, major reamputation rate, and complication rate.

METHODS

939 patients admitted to the Department of Orthopaedic Surgery, NUH, for DFP from 2002 (before team formation) to 2007 (after team formation). It consisted of six cohorts of patients - 61 for 2002, 70 for 2003, 148 for 2004, 180 for 2005, 262 for 2006, and 218 for 2007. All patients were managed by the NUH Multidisciplinary Diabetic Foot Team combined with a clinical pathway. Statistical analyses were carried out for five parameters (ALOS, hospitalisation cost per patient, major amputation rate, readmission rate, and complication rate).

RESULTS

From 2002 to 2007, the ALOS was significantly reduced from 20.36 days to 12.20 days (p=0.0005). Major amputation rate was significantly reduced from 31.15 to 11.01% (p<0.0005). There was also a significant reduction in complication rate from 19.67 to 7.34% (p=0.005). There were reductions in the hospitalisation cost per patient and readmission rate after formation of the multidisciplinary team but they were not statistically significant.

CONCLUSION

Our evaluation showed that a multidisciplinary team approach combined with the implementation of a clinical pathway in NUH was effective in reducing the ALOS, major amputation rate, and complication rate of DFP.

Crossing the evidence chasm: building evidence bridges from process changes to clinical outcomes

OBJECTIVE

Although demand for information about the effectiveness and efficiency of health care information technology grows, large-scale resource-intensive randomized controlled trials of health care information technology remain impractical. New methods are needed to translate more commonly available clinical process measures into potential impact on clinical outcomes.

DESIGN

The authors propose a method for building mathematical models based on published evidence that provides an evidence bridge between process changes and resulting clinical outcomes. This method combines tools from systematic review, influence diagramming, and health care simulations.

MEASUREMENTS

The authors apply this method to create an evidence bridge between retinopathy screening rates and incidence of blindness in diabetic patients.

RESULTS

The resulting model uses changes in eye examination rates and other evidence-based population parameters to generate clinical outcomes and costs in a Markov model.

CONCLUSION

This method may serve as an alternative to more expensive study designs and provide useful estimates of the impact of health care information technology on clinical outcomes through changes in clinical process measures.

Health care resource utilization associated with a diabetes center and a general medicine clinic

OBJECTIVE

Studies have proposed that the features of diabetes clinics may decrease hospital utilization and costs by reducing complications and providing more efficient outpatient care. We compared the health care utilization associated with a diabetes center (DC) and a general medicine clinic (GMC).

DESIGN

Retrospective cohort study.

SETTING

An urban academic medical center.

PATIENTS/PARTICIPANTS

Type 2 diabetes patients (N = 601) under care in a DC and GMC before March 1996.

MEASUREMENTS AND MAIN RESULTS

We compared baseline patient characteristics and outpatient care for the period of March 1996 to August 1997. Using administrative data from March 1996 to October 2000, we compared the probability of a hospitalization, length of stay, costs of hospitalizations, the probability of an emergency room visit, and costs of emergency room visits. Diabetes center patients had a longer mean duration of diabetes (12 years vs 6 years, P <.01), more baseline microvascular disease (65% vs 44%, P <.01), and higher baseline glucose levels (hemoglobin A1c 8.6% vs 7.9%, P <.01) than GMC patients. Diabetes center patients received more intensive outpatient care directed toward glucose monitoring and control. In all crude and adjusted analyses of hospitalizations and emergency room visits, we found no statistically significant differences for inpatient utilization or cost outcomes comparing clinic populations.

CONCLUSIONS

Diabetes center attendance did not have a definitive positive or negative impact on inpatient resource utilization over a 4-year period. However, DC patients had more severe diabetes but no greater hospital utilization compared with GMC patients. Clear demonstration of the clinical and financial benefits of features of diabetes centers will require long-term controlled trials of interventions that promote comprehensive diabetes care, including cardiovascular prevention.

Review of evidence and explanations for suboptimal screening and treatment of dyslipidemia in women. A conceptual model

Screening and treatment rates for dyslipidemia in populations at high risk for cardiovascular disease (CVD) are inappropriately low and rates among women may be lower than among men. We conducted a review of the literature for possible explanations of these observed gender differences and categorized the evidence in terms of a conceptual model that we describe. Factors related to physicians' attitudes and knowledge, the patient's priorities and characteristics, and the health care systems in which they interact are all likely to play important roles in determining screening rates, but are not well understood. Research and interventions that simultaneously consider the influence of patient, clinician, and health system factors, and particularly research that focuses on modifiable mechanisms, will help us understand the causes of the observed gender differences and lead to improvements in cholesterol screening and management in high-risk women. For example, patient and physician preferences for lipid and other CVD risk factor management have not been well studied, particularly in relation to other gender-specific screening issues, costs of therapy, and by degree of CVD risk; better understanding of how available health plan benefits interact with these preferences could lead to structural changes in benefits that might improve screening and treatment.

Case-control study of 10 years of comprehensive diabetes care

OBJECTIVE

To describe the long-term clinical impact of a comprehensive management program instituted throughout a health system for members with diabetes mellitus.

DESIGN

10 year case-control evaluation.

SETTING

Kaiser Permanente Northwest, Portland, OR.

PARTICIPANTS

Members of the health maintenance organization between 1987 and 1996; members with diabetes were compared with equal numbers of members without diabetes. The number of participants with diabetes ranged from 5331 in 1987 to 13,099 in 1996.

MAIN OUTCOME MEASURES

Number in diabetes register, mortality, change in comorbidity, rates of uptake of preventive health measures, use of pharmaceuticals, levels of risk factors, hospital days per thousand per year, emergency room visits per thousand per year.

RESULTS

The prevalence of diabetes identified in this population rose from 2.54% (7,895/310,819) in 1987 to 3.66% (14,741/402,754) in 1996, and the mean (SEM) age of members at the time of diagnosis fell slightly from 62.9 (+/- 0.21) years to 62.0 (+/- 0.13) years (P < 0.05). By 1996, 10,885 of the 13,099 (83% +/- 0.3%) of members with diabetes had an annual laboratory test to assess glycemic control, the annual screening rate for retinopathy was 67.6% (+/- 0.4%), the rate of uptake of influenza immunizations was 60.2% (7,886/13,099) and the screening rate for nephropathy was 43% (5,698/13,099) (+/- 0.49%). The use of home glucose testing increased from 32.4% (1721/5331) of members with diabetes to 53.0% (6,942/12,099); the use of lipid lowering drugs increased from 3.5% (187/55,331) to 19.8% (2,594/13,099). The use of angiotensin converting enzyme inhibitors increased from 8.5% to 34.8% of members with diabetes. Mean blood pressure decreased from 144/82 mm Hg (+/- 0.8/0.4) to 138/79 mm Hg (+/- 0.3/0.15), and mean total cholesterol concentrations dropped from 243 mg/dL (+/- 4.2) to 215 mg/dL (+/- 0.6). By 1996, 56.4% (7,388/1,3099) (+/- 0.5%) of members on the diabetes register had good to excellent glycemic control (HbA1c < 8%). Mortality decreased from 4.8% (256/5331) (+/- 0.3%) to 3.6% (472/13,099) (+/- 0.2%) among members with diabetes, this was a more rapid decrease than was observed among those without diabetes (P < 0.01). The annual ratio of visits to the emergency room by members with diabetes to members without fell from 2.5 to 1.8, and the ratio for the number of days spent in acute care in the hospital dropped from 3.6 to 2.5.

CONCLUSIONS

This centrally organized program based in a primary care setting and utilizing a register of patients with diabetes was associated with substantial improvements in the process and outcomes of care in a large population of health maintenance organization members with diabetes.