Peter Dews

The effect of patient and physician reminders on use of screening mammography in a health maintenance organization: Results of a randomized controlled trial

Despite its demonstrated efficacy in reducing breast carcinoma mortality, screening mammography remains underutilized and its promotion in the primary care setting provides an important opportunity for intervention.

A Potential Causal Association Mining Algorithm for Screening Adverse Drug Reactions in Postmarketing Surveillance

Early detection of unknown adverse drug reactions (ADRs) in postmarketing surveillance saves lives and prevents harmful consequences. We propose a novel data mining approach to signaling potential ADRs from electronic health databases. More specifically, we introduce potential causal association rules (PCARs) to represent the potential causal relationship between a drug and ICD-9 (CDC. (2010). International Classification of Diseases, Ninth Revision (ICD-9). [Online]. Available: http://www.cdc.gov/nchs/icd/icd9.html) coded signs or symptoms representing potential ADRs. Due to the infrequent nature of ADRs, the existing frequency-based data mining methods cannot effectively discover PCARs. We introduce a new interestingness measure, potential causal leverage, to quantify the degree of association of a PCAR. This measure is based on the computational, experience-based fuzzy recognition-primed decision (RPD) model that we developed previously (Y. Ji, R. M. Massanari, J. Ager, J. Yen, R. E. Miller, and H. Ying, “A fuzzy logic-based computational recognition-primed decision model,” Inf. Sci., vol. 177, pp. 4338-4353, 2007) on the basis of the well-known, psychology-originated qualitative RPD model (G. A. Klein, “A recognition-primed decision making model of rapid decision making,” in Decision Making in Action: Models and Methods, 1993, pp. 138-147). The potential causal leverage assesses the strength of the association of a drug-symptom pair given a collection of patient cases. To test our data mining approach, we retrieved electronic medical data for 16 206 patients treated by one or more than eight drugs of our interest at the Veterans Affairs Medical Center in Detroit between 2007 and 2009. We selected enalapril as the target drug for this ADR signal generation study. We used our algorithm to preliminarily evaluate the associations between enalapril and all the ICD-9 codes associated with it. The experimental results indicate that our approach has a potential to better signal potential ADRs than risk ratio and leverage, two traditional frequency-based measures. Among the top 50 signal pairs (i.e., enalapril versus symptoms) ranked by the potential causal-leverage measure, the physicians on the project determined that eight of them probably represent true causal associations.

How reminders given to patients and physicians affected Pap smear use in a health maintenance organization: Results of a randomized controlled trial

Despite its effectiveness as a method of controlling cervical carcinoma, the use of Pap smear testing remains incomplete, and its promotion in the primary care setting provides an important opportunity for intervention.

The effect of adding Pap smear information to a mammography reminder system in an HMO: results of randomized controlled trial

BACKGROUND While reminders can promote cancer screening in primary care, little is known about the potential interaction between multiple reminders. METHODS We conducted a randomized controlled trial to compare the effect of combined Pap smear plus mammogram reminders and mammogram-only reminders among 2471 women 40 years of age or older enrolled in a health maintenance organization serving a predominantly Medicaid-eligible population. Reminders included both a mailed letter for the woman and a medical record prompt. RESULTS Intervention assignment was unassociated with differences in rates of visitation to family medicine or internal medicine or completion of mammography during the study year. Compared to women assigned to mammogram-only reminder treatment, those assigned to the combined Pap smear plus mammogram reminder intervention were more likely to visit a gynecologist (34% compared to 29%, adjusted odds ratio = 1.33, 95% confidence interval 1.08-1.63) and to complete a Pap smear (30% compared to 23%, adjusted odds ratio = 1.39, 95% confidence interval 1.07-1.89). CONCLUSIONS In the study setting, the addition of Pap smear to mammography reminders has a procedure-specific effect, increasing gynecology visits and Pap smear use while neither increasing nor decreasing other primary care visits or mammography. We find no evidence of reinforcement or competition between these reminders.

A Method for Mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs

In many real-world applications, it is important to mine causal relationships where an event or event pattern causes certain outcomes with low probability. Discovering this kind of causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient data sets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The algorithm was tested on real patient data retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The retrieved data included 16,206 patients (15,605 male, 601 female). The exclusive causal-leverage was employed to rank the potential causal associations between each of the three selected drugs (i.e., enalapril, pravastatin, and rosuvastatin) and 3,954 recorded symptoms, each of which corresponded to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by the physicians on our project team. The numbers of symptoms considered as likely real ADRs for enalapril, pravastatin, and rosuvastatin were 8, 7, and 6, respectively. These preliminary results indicate the usefulness of our method in finding potential ADR signal pairs for further analysis (e.g., epidemiology study) and investigation (e.g., case review) by drug safety professionals.

A Distributed, Collaborative Intelligent Agent System Approach for Proactive Postmarketing Drug Safety Surveillance

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<10% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent software system approach for proactively monitoring and detecting potential ADRs of interest using electronic patient records. We designed such a system and named it ADRMonitor. The intelligent agents, operating on computers located in different places, are capable of continuously and autonomously collaborating with each other and assisting the human users (e.g., the food and drug administration (FDA), drug safety professionals, and physicians). The agents should enhance current systems and accelerate early ADR identification. To evaluate the performance of the ADRMonitor with respect to the current spontaneous reporting approach, we conducted simulation experiments on identification of ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions) under various conditions. The experiments involved over 275 000 simulated patients created on the basis of more than 1000 real patients treated by the drug cisapride that was on the market for seven years until its withdrawal by the FDA in 2000 due to serious ADRs. Healthcare professionals utilizing the spontaneous reporting approach and the ADRMonitor were separately simulated by decision-making models derived from a general cognitive decision model called fuzzy recognition-primed decision (RPD) model that we recently developed. The quantitative simulation results show that 1) the number of true ADR signal pairs detected by the ADRMonitor is 6.6 times higher than that by the spontaneous reporting strategy; 2) the ADR detection rate of the ADRMonitor agents with even moderate decision-making skills is five times higher than that of spontaneous reporting; and 3) as the number of patient cases increases, ADRs could be detected significantly earlier by the ADRMonitor.

The effect of patient reminders on the use of screening mammography in an urban health department primary care setting

Mammography screening continues to be under-utilized, especially among women from lower socioeconomic groups. In order to determine whether having direct access to health care services has an effect on mammography use among low income women, we conducted a randomized trial of two alternative letter reminders among 1,717 women who were enrolled at two locations of a multi-site inner city health department in Detroit. All participants were 39 1/2 years of age and older and were due for a screening mammogram at randomization. A physician-directed reminder form was placed in each of the participant’s medical records at the beginning of the study. In addition participants were randomized to receive either a letter directing them to visit their primary care physician, a letter directing them to contact the clinic directly to schedule a mammogram, or no letter. Study participants were predominantly African–American, two-thirds of whom were over age 50, and who had minimal health insurance coverage. During the intervention year, mammograms were completed by 179 out of 967 study women at site one (18.5%), and 90 out of 750 study women at site two (12%). A multivariate model controlling for the simultaneous effect of age, insurance type, visit history and past mammography use, showed no significant independent effect of either type of letter reminder on mammography completion during the study year. In conclusion, letters targeted at women due for screening mammograms did not have a beneficial effect on mammography utilization above and beyond that of a physician medical record reminder.

A fuzzy recognition-primed decision model-based causal association mining algorithm for detecting adverse drug reactions in postmarketing surveillance

The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<10% reporting rate), latency, and inconsistent reporting. We propose a new interestingness measure, causal-leverage, to signal potential adverse drug reactions (ADRs) from electronic health databases which are readily available in most modern hospitals. This measure is based on an experience-based fuzzy recognition-primed decision (RPD) model that we developed previously [1] which assesses the strength of association of a drug-ADR pair within each individual patient case. Using the causal-leverage measure, we develop a data mining algorithm to evaluate the associations between a given drug enalapril and all potential ADRs in a real-world electronic health database. The experimental results have shown that our approach can effectively shortlist some known ADRs. For example, the known ADR hyperkalemia caused by enalapril was ranked as top 1% among all the 3954 potential ADRs in our database.

Mining Infrequent Causal Associations in Electronic Health Databases

Discovering infrequent causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient datasets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The algorithm was tested on real patient data retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The exclusive causal-leverage was employed to rank the potential causal associations between each of the two selected drugs (i.e., enalapril and pravastatin) and 3,954 recorded symptoms, each of which corresponds to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by our physicians on the project team. The results showed that the number of symptoms considered as real ADRs for enalapril and pravastatin was 8 and 7 out of 10, respectively.

Finding similar patients in a multi-agent environment

Finding similar patients is highly desirable in many clinical applications. In this paper, we address the issue of how to find similar patients in a multi-agent environment where software agents, located in different places, work collaboratively and proactively help one another to empower their human users to achieve a common healthcare goal. We show how the agents, equipped with fuzzy similarity rules developed by the physicians on the team, collaborate to find similar patients in each agents patient database. We describe the architecture, design and implementation of the system. Using the popular agent language JADE and clinical information on 1,000 patients treated at the Detroit Veterans Affairs Medical Center, we have implemented a five-agent system and generated some preliminary simulation results.