Knowledge Engineering for Decision Support in Osteoporosis (Utah)

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Summary:

Despite the availability of effective treatments for men to prevent osteoporotic fractures, rates of diagnosis, screening, and treatment remain low. Both the National Osteoporosis Foundation and the Endocrine Society recommend universal bone densitometry in men over 70, with treatment for both those with osteoporosis and osteopenia as guided by fracture absolute risk assessment (FARA). The overall objective of this project was to develop a Veterans Affairs (VA)-optimized fracture absolute risk assessment (VA-FARA) rule for identifying males at highest risk of osteoporotic fracture.

The specific aims of the project were as follows:

  • Develop a VA-optimized risk stratification rule for fracture. 
  • Develop a VA-optimized clinical decision rule. 
  • Develop a decision support tool designed to facilitate appropriate prophylaxis of osteoporotic fractures. 

The investigators developed, validated, and calibrated the rule using national VA datasets of risk factors collected as a routine part of health care operations. The rule was piloted in one of the Veterans Integrated Service Networks, where they compared clinical strategies for the rule at different absolute risk thresholds. In addition, the investigators conducted clinician focus groups to identify implementation barriers of the rule. 

The investigators found that the rule was superior for identifying highest-risk patients and that treating high-risk patients regardless of bone mineral density was most effective and least costly. However, the focus groups indicated that there may be resistance to implementing this strategy, as there was low clinical acceptance of treating high-risk patients before they developed osteoporosis.

Knowledge Engineering for Decision Support in Osteoporosis - 2012

Summary Highlights

  • Principal Investigator: 
  • Organization: 
  • Funding Mechanism: 
    PAR: HS09-085: Mentored Clinical Scientist Research Career Development Award (K08)
  • Grant Number: 
    K08 HS 018582
  • Project Period: 
    January 2010 – December 2014
  • AHRQ Funding Amount: 
    $805,680
  • PDF Version: 
    (PDF, 208.52 KB)

Summary: There are many barriers to the diagnosis and treatment of osteoporosis. These include information and cognitive barriers such as the failure to identify that a patient is at high risk for a fragility fracture, not knowing what level of risk justifies treatment, and uncertainty about when to initiate treatment. These are some of the reasons that fewer than 25 percent of veterans who are at risk for fracture are currently treated for osteoporosis. While computerized clinical-decision support has the potential to improve appropriate treatment rates by identifying patients at risk, such systems are often poorly developed and may not reflect physicians’ models for conducting clinical tasks or preferences for structuring tasks and navigating systems, thus reducing the system’s optimal impact.

The overall goal of this project is to create a method for designing osteoporosis-related treatment decision support that incorporates the needs of clinicians in order to minimize cognitive burden. Dr. Lafleur and her team are using electronic and survey data to create a new risk-stratification rule. This rule will adapt a currently accepted risk-stratification rule and the World Health Organization’s treatment guidelines for the veteran population, and identify information constructs that help clinicians make correct treatment decisions. These findings will inform the development and pilot testing of a new tool. While this project is focused on a specific clinical topic and setting, its approach to providing decision support at the point of care by integrating treatment guidelines, characteristics of the target population, and information needs of clinicians, could become a decision-support template for other diseases and conditions.

Specific Aims:

  • Create and validate a Veterans’ Affairs (VA)-specific risk-stratification rule for fragility fractures. (Ongoing)
  • Incorporate the risk-stratification rule into a computerized decision-support system for osteoporosis treatment. (Ongoing)
  • Pilot the decision support tool for initiating osteoporosis treatment. (Upcoming)

In addition to the research project goals, Dr. LaFleur is working toward her long-term career goal of identifying and preventing drug-therapy failures in chronic disease populations. Funding from this Mentored Clinical Scientist Research Career Development Award helps Dr. LaFleur advance her skills in health services research through structured coursework, regular seminars, and mentoring in the fields of clinical informatics, decision modeling, epidemiologic methods, and statistical approaches.

2012 Activities: The development of the risk-stratification rule was completed. The dataset combines variables from four datasets including Medicare data and three VA datasets: 1) the Medical SAS Dataset, which includes all inpatient and outpatient services provided to veterans; 2) the Corporate Data Warehouse, which includes clinical patient care information from the Veteran’s Health Information Systems and Technology Architecture (VistA), the VA’s electronic health record; and 3) the Pharmacy Benefits Management Dataset, which includes records of prescriptions dispensed to veterans to identify drug exposures related to risk. The model incorporates outcome data from the Medical SAS dataset for fractures that were treated within the VA system, and outcome data from the Medicare-VA dataset to capture fractures that were treated outside the VA system. The next step is to validate the rule by comparing the risk estimate of the tool to the Fracture Risk Assessment Tool. To capture risk data, the project is using a group of pharmacy residents surveying patients from the Sierra Nevada region about their risk factors for fracture.

Dr. LaFleur has begun work on the second aim, which involves conducting focus groups with providers and developing a series of case vignettes to identify risk factors and fracture risk constructs that are associated with osteoporosis treatment. These fictional patient cases are designed to include information that clinicians would typically have at their disposal when seeing patients, in order to allow Dr. LaFleur to understand what kinds of clinical information are the most important when providers make decisions about osteoporosis. By the end of 2012, five of six focus groups with providers were completed.

Preliminary Impact and Findings: Dr. Lafleur and her team have developed a discriminating tool for identifying male veterans at highest risk of fracture. The tool is being used in a decision-support dashboard for osteoporosis management in the Sierra Pacific VA health care network. They have used decision analysis to test the tool for optimal thresholds of absolute risk at which patients should be treated and also to identify an optimal treatment strategy.

Target Population: Adults, Osteoporosis, Veterans

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

Knowledge Engineering for Decision Support in Osteoporosis - 2011

Summary Highlights

  • Principal Investigator: 
  • Organization: 
  • Funding Mechanism: 
    PAR: HS09-085: Mentored Clinical Scientist Research Career Development Award (K08)
  • Grant Number: 
    K08 HS 018582
  • Project Period: 
    January 2010 - December 2014
  • AHRQ Funding Amount: 
    $805,680
  • PDF Version: 
    (PDF, 179.6 KB)

Summary: There are many barriers to the diagnosis and treatment of osteoporosis. They include process barriers, such as workflow and organization, and information and cognitive barriers, such as the saliency of the problem and suboptimal organization of information relevant to the treatment decision. Specific cognitive barriers to identifying and treating osteoporosis include failure to identify that a patient is at high risk for a fragility fracture, not knowing what level of risk justifies treatment, and uncertainty about when to initiate treatment. These are some of the reasons why, despite the high burden of osteoporosis, fewer than 25 percent of veterans who are at risk for fracture are currently treated for osteoporosis.

While computerized clinical decision support has the potential to improve appropriate treatment rates by identifying patients at risk, such systems are often poorly developed and may not reflect physicians' models for conducting clinical tasks or preferences for structuring tasks and navigating systems, thus reducing the system's optimal impact.

The overall goal of this project is to develop robust knowledge for supporting accurate osteoporosis-related treatment decisions to address these information barriers. Dr. LaFleur and her team are using electronic and survey data to create a new risk-stratification rule. This rule will adapt a currently accepted riskstratification rule and the World Health Organization's treatment guidelines for the veteran population, identify information constructs that are important to clinicians for supporting the correct treatment decision. The findings will inform the development and pilot testing of a new tool.

While this project is focused on a specific clinical topic and setting, its approach to providing decision support at the point of care by integrating treatment guidelines, characteristics of the target population, and information needs of clinicians, could become a decision support template for other diseases and conditions.

Specific Aims:

  • Create and validate a Veterans' Affairs (VA)-specific risk-stratification rule for fragility fractures. (Ongoing)
  • Incorporate the risk-stratification rule into a computerized decision support system for osteoporosis treatment. (Ongoing)
  • Pilot the decision support tool for initiating osteoporosis treatment. (Upcoming)

In addition to the research project goals, Dr. LaFleur will further her long-term career goal of identifying and preventing drug-therapy failures in chronic disease populations. Funding from this Mentored Clinical Scientist Research Career Development Award will allow Dr. LaFleur to advance her skills in health services research through structured coursework, regular seminars, and mentoring in the fields of clinical informatics, decision modeling, epidemiologic methods, and statistical approaches.

2011 Activities: The development of the risk-stratification rule was almost complete during the year pending the addition of Medicare data to the dataset. The dataset currently combines variables related to fracture risk from three Veterans Affairs (VA) datasets: 1) the Medical SAS Dataset (all inpatient and outpatient services provided to veterans); 2) the Corporate Data Warehouse (clinical patient care information from VistA, the VA's electronic health record); and 3) the Pharmacy Benefits Management Dataset (records of prescriptions dispensed to veterans to identify drug exposures related to risk). The model incorporates outcome data from the Medical SAS dataset for fractures that were treated within the VA system, and outcome data from the Medicare-VA dataset to capture fractures that were treated outside the VA system. It took several months to receive approval from the Centers for Medicare and Medicaid Services (CMS) to access and integrate Medicare data into the tool. At the end of the year, Dr. LaFleur was still waiting to receive the Medicare data. During the year, she developed the preliminary models to validate the rule that does not contain the CMS outcomes, including models for bone mineral density, body mass index, smoking history, and family history of fracture. Once the CMS data are integrated into the models, Dr. LaFleur will calibrate the rule. This involves surveying veterans on risk factors for fracture using an existing survey that the project team adapted to assess risk factors for osteoporosis.

Even though the development of the rule is not complete, Dr. LaFleur has been able to begin the second aim, which involves conducting focus groups with providers and developing a series of case vignettes to identify risk factors and fracture risk constructs that are associated with osteoporosis treatment. These fictional patient cases are designed to include information that clinicians would typically have at their disposal when seeing patients. This will allow Dr. LaFleur to ask providers questions about the kinds of clinical information that are most important to help them make decisions about osteoporosis. The study team is scheduling focus groups for three sites, two VA and one non-VA site, to be conducted in early 2012.

One change from the original grant proposal was the addition into the rule of bone mineral density screening. While bone mineral density screenings are predictive of fracture risk, they are not codified anywhere in the electronic data. However, Dr. LaFleur and her team used natural-language processing software to integrate these screenings into the model as a variable. Dr. LaFleur presented this process at the American Society of Bone Mineral Research meeting in September 2011.

Preliminary Impact and Findings: This project has no findings to date.

Target Population: Adults, Osteoporosis, Veterans

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

Knowledge Engineering for Decision Support in Osteoporosis - 2010

Summary Highlights

  • Principal Investigator: 
  • Organization: 
  • Funding Mechanism: 
    PAR: HS09-085: Mentored Clinical Scientist Research Career Development Award (K08)
  • Grant Number: 
    K08 HS 018582
  • Project Period: 
    January 2010 – December 2014
  • AHRQ Funding Amount: 
    $805,680
  • PDF Version: 
    (PDF, 296.99 KB)


Target Population: Osteoporosis, Veterans

Summary: There are many factors that influence how and if chronic diseases such as osteoporosis are identified and treated. They include process barriers, such as workflow and organization, and information and cognitive barriers, such as the saliency of the problem and suboptimal organization of information relevant to the treatment decision. Specific cognitive barriers to identifying and treating osteoporosis include failure to identify that a patient is at high risk for a fragility fracture, not knowing what level of risk justifies treatment, and uncertainty about when to initiate treatment. This is one of the reasons why, despite the high burden of osteoporosis, fewer than 25 percent of veterans who are at risk for fracture are currently treated for osteoporosis.

While computerized clinical decision support has the potential to improve appropriate treatment rates by identifying patients at risk, such systems are often poorly developed and may not reflect physicians’ models for conducting clinical tasks or preferences for structuring tasks and navigating systems, thus reducing the system’s optimal impact.

The overall goal of this project is to develop robust knowledge for supporting accurate osteoporosis-related treatment decisions that addresses these information barriers. Specifically, the investigators will use electronic and survey data to create a new risk-stratification rule. This rule will adapt a currently accepted risk-stratification rule and the World Health Organization’s treatment guidelines to the veteran population, identify information constructs that are important to clinicians for supporting the correct treatment decision, and use the findings to develop and pilot test a new tool.

While this project is focused on a specific clinical topic and setting, its approach to providing decision support at the point of care by integrating treatment guidelines, characteristics of the target population, and information needs of clinicians can serve as template for decision support for other disease conditions.

Specific Aims:
  • Create and validate a Veterans’ Affairs (VA)-specific risk-stratification rule for fragility fractures. (Ongoing)
  • Incorporate the risk-stratification rule into a computerized decision support system for osteoporosis treatment. (Ongoing)
  • Pilot the decision support tool for initiating osteoporosis treatment. (Upcoming)

In addition to the research project goals, Dr. LaFleur will further her long-term career goal of identifying and preventing drug-therapy failures in chronic disease populations. Funding from this Mentored Clinical Scientist Research Career Development Award will allow Dr. LaFleur to advance her skills in health services research through structured coursework, regular seminars, and mentoring in the fields of clinical informatics, decision modeling, epidemiologic methods, and statistical approaches.

2010 Activities: 2010 activities focused on developing the risk stratification rule to be used with VA coded data. Dr. LaFleur and her team identified a cohort of 2.9 million veterans and completed a significant amount of the programming and analysis to develop the risk stratification models. This included developing a dataset that combines variables related to fracture risk from three VA datasets: the Medical SAS Dataset (all inpatient and outpatient services provided to veterans), the Corporate Data Warehouse (clinical patient care information from VistA), and the Pharmacy Benefits Management Dataset (records of prescriptions dispensed to veterans to identify drug exposures related to risk). The model incorporates outcome data from the Medical SAS dataset for fractures that were treated within the VA system and outcome data from the Medicare-VA dataset to capture fractures that were treated outside the VA system. The team developed and submitted their request for the Medicare data and are awaiting approval.

One change from the original grant proposal was the addition into the rule of bone mineral density screening. While bone mineral density screenings are predictive of fracture risk, they are not codified anywhere in the electronic data. However, Dr. LaFleur and her team used natural language processing software to integrate these screenings into the model as a variable.

Work began on incorporating the risk-stratification rule into a computerized decision support system with the development of case vignettes to identify risk factors and fracture risk constructs that are associated with osteoporosis treatment. In addition, and as part of her career goal for the Mentored Clinical Scientist Research Career Development Award, Dr. LaFleur completed the first year of courses towards her 3-year informatics certificate.

Preliminary Impact and Findings: There are no preliminary findings.

Strategic Goal: Develop and disseminate health IT evidence and evidence-based tools to improve health care decisionmaking through the use of integrated data and knowledge management.

Business Goal: Knowledge Creation

Knowledge Engineering for Decision Support in Osteoporosis - Final Report

Citation:
LaFleur J. Knowledge Engineering for Decision Support in Osteoporosis - Final Report. (Prepared by the University of Utah College of Pharmacy under Grant No. K08 HS018582). Rockville, MD: Agency for Healthcare Research and Quality, 2015. (PDF, 1.29 MB)

The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
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