What are the effects of integrated disease management (IDM) interventions for people with chronic obstructive pulmonary disease (COPD)?

2021 ◽  
Author(s):  
Sera Tort ◽  
Karen Pettersen
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Disease Management ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Integrated Disease Management ◽  
Obstructive Pulmonary Disease

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

10.2196/27065 ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. e27065
Author(s):  
Gang Luo ◽  
Bryan L Stone ◽  
Xiaoming Sheng ◽  
Shan He ◽  
Corinna Koebnick ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Health Care ◽  
High Risk ◽  
Disease Management ◽  
Pulmonary Disease ◽  
Administrative Data ◽  
Chronic Obstructive ◽  
Integrated Disease Management ◽  
Obstructive Pulmonary Disease ◽  
Temporal Features

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

scholarly journals Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis (Preprint)

2021 ◽  
Author(s):  
Gang Luo ◽  
Bryan L Stone ◽  
Xiaoming Sheng ◽  
Shan He ◽  
Corinna Koebnick ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Health Care ◽  
High Risk ◽  
Disease Management ◽  
Pulmonary Disease ◽  
Administrative Data ◽  
Chronic Obstructive ◽  
Integrated Disease Management ◽  
Obstructive Pulmonary Disease ◽  
Temporal Features

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 PRR1-10.2196/27065

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