DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety.
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ABSTRACT:
SUBMITTER: Xiong G
PROVIDER: S-EPMC8728114 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWarnings about drug-drug interactions (DDIs) between warfarin and nonsteroidal anti-inflammatory drugs (NSAIDs) within electronic health records indicate potential harm but fail to account for contextual factors and preferences. We developed a tool called DDInteract to enhance and support shared decision-making (SDM) between patients and physicians when both warfarin and NSAIDs are used concurrently. DDInteract was designed to be integrated into electronic health records using interoperability standards.ObjectiveThe purpose of this study was to conduct a formative evaluation of a DDInteract that incorporates patient and product contextual factors to estimate the risk of bleeding.MethodsA randomized formative evaluation was conducted to compare DDInteract to usual care (UC) using physician-patient dyads. Using case vignettes, physicians and patients on warfarin participated in simulated virtual clinical encounters where they discussed the use of taking ibuprofen and warfarin concurrently and determined an appropriate therapeutic plan based on the patient's individualized risk. Dyads were randomized to either DDInteract or UC. Participants completed a postsession interview and survey of the SDM process. This included the 9-item Shared Decision-Making Questionnaire (SDM-Q-9), tool usability and workload National Aeronautics and Space Administration (NASA) Task Load Index, Unified Theory of Acceptance and Use of Technology (UTAUT), Perceived Behavioral Control (PBC) scale, System Usability Scale (SUS), and Decision Conflict Scale (DCS). They also were interviewed after the session to obtain perceptions on DDInteract and UC resources for DDIs.ResultsTwelve dyad encounters were performed using virtual software. Most (n=11, 91.7%) patients were over 50 years of age, and 9 (75%) had been taking warfarin for more than 2 years (75%). Regarding scores on the SDM-Q-9, participants rated DDInteract higher than UC for questions pertaining to helping patients clarify the decision (P=.03), involving patients in the decision (P=.01), displaying treatment options (P<.001), identifying advantages and disadvantages (P=.01), and facilitating patient understanding (P=.01) and discussion of preferences (P=.01). Five of the 8 UTAUT constructs showed differences between the 2 groups, favoring DDInteract (P<.05). Usability ratings from the SUS were significantly higher (P<.05) for physicians using DDInteract compared to those in the UC group but showed no differences from the patient's perspective. No differences in patient responses were observed between groups using the DCS. During the session debrief, physicians indicated little concern for the additional time or workload entailed by DDInteract use. Both clinicians and patients indicated that the tool was beneficial in simulated encounters to understand and mitigate the risk of harm from this DDI.ConclusionsOverall, DDInteract may improve encounters where there is a risk of bleeding due to a potential drug-drug interaction involving anticoagulants. Participants rated DDInteract as logical and useful for enhancing SDM. They reported that they would be willing to use the tool for an interaction involving warfarin and NSAIDs.
| S-EPMC9631167 | biostudies-literature
Project description:The integration of face-to-face communication and online processes to provide access to information and self-assessment tools may improve shared decision-making (SDM) processes. We aimed to assess the effectiveness of implementing an online SDM process with topics and content developed through a participatory design approach. We analyzed the triggered and completed SDM cases with responses from participants at a medical center in Taiwan. Data were retrieved from the Research Electronic Data Capture (REDCap) database of the hospital for analysis. Each team developed web-based patient decision aids (PDA) with empirical evidence in a multi-digitized manner, allowing patients to scan QR codes on a leaflet using their mobile phones and then read the PDA content online. From July 2019 to December 2020, 48 web-based SDM topics were implemented in the 24 clinical departments of this hospital. The results showed that using the REDCap system improved SDM efficiency and quality. Implementing an online SDM process integrated with face-to-face communication enhanced the practice and effectiveness of SDM, possibly through the flexibility of accessing information, self-assessment, and feedback evaluation.
| S-EPMC8879344 | biostudies-literature
Project description:Study objectivesPerson-centered obstructive sleep apnea (OSA) care is a collaborative approach that is respectful of an individual's health priorities. Informed decision-making is essential to person-centered care, especially as patients age. In a feasibility study, we evaluated the effects of a new decision aid (Decide2Rest) on OSA treatment decision-making in older adults.MethodsPatients (aged ≥ 60 years) with newly diagnosed OSA were recruited from two health care systems and randomized either to Decide2Rest or to a control program. Postintervention outcomes included 1) Decisional Conflict Scale (0-100, where 0 = low and 100 = high conflict), which measures perceptions of uncertainty, whether decisions reflect what matters most to patients, and whether patients feel supported in decision-making; 2) Preparation for Decision-Making scale (0-100, where 0 = least and 100 most prepared); and 3) OSA knowledge (0-100, where 0 = poor and 100 = outstanding). Multivariable linear regression models examined relationships between Decide2Rest and outcomes (Decisional Conflict Scale, Preparation for Decision-Making, OSA knowledge).ResultsSeventy-three patients were randomized to Decide2Rest (n = 36; mean age, 69 years; 72% male) vs control (n = 37; mean age, 69 years; 70% male). Results from the regressions, controlling for study site, indicate that the Decide2Rest program resulted in less decisional conflict (20.5 vs 32.7 on the Decisional Conflict Scale; P = .014), more preparedness for decision-making (87.8 vs 66.2 on the Preparation for Decision-Making scale; P < .001), and greater OSA knowledge (75.1 vs 65.3 OSA knowledge score; P = .04) scores than in the control group.ConclusionsThe Decide2Rest program promotes person-centered OSA decision-making for older patients with newly diagnosed OSA. Future studies are needed to optimize implementation of the program.Clinical trial registrationRegistry: ClinicalTrials.gov, Name: Improving Older Adults' Decision-Making for OSAT (eDecide2Rest); URL: https://clinicaltrials.gov/ct2/show/NCT03138993; Identifier: NCT03138993.
| S-EPMC7853212 | biostudies-literature
Project description:BackgroundPatient decision aids (PDAs) can support the treatment decision making process and empower patients to take a proactive role in their treatment pathway while using a shared decision-making (SDM) approach making participatory medicine possible. The aim of this study was to develop a PDA for prostate cancer that is accurate and user-friendly.MethodsWe followed a user-centered design process consisting of five rounds of semi-structured interviews and usability surveys with topics such as informational/decisional needs of users and requirements for PDAs. Our user-base consisted of 8 urologists, 4 radiation oncologists, 2 oncology nurses, 8 general practitioners, 19 former prostate cancer patients, 4 usability experts and 11 healthy volunteers.ResultsInformational needs for patients centered on three key factors: treatment experience, post-treatment quality of life, and the impact of side effects. Patients and clinicians valued a PDA that presents balanced information on these factors through simple understandable language and visual aids. Usability questionnaires revealed that patients were more satisfied overall with the PDA than clinicians; however, both groups had concerns that the PDA might lengthen consultation times (42 and 41%, respectively). The PDA is accessible on http://beslissamen.nl/ .ConclusionsUser-centered design provided valuable insights into PDA requirements but challenges in integrating diverse perspectives as clinicians focus on clinical outcomes while patients also consider quality of life. Nevertheless, it is crucial to involve a broad base of clinical users in order to better understand the decision-making process and to develop a PDA that is accurate, usable, and acceptable.
| S-EPMC6624887 | biostudies-literature
Project description:BackgroundChronic pain has emerged as a disease in itself, affecting a growing number of people. Effective patient-provider communication is central to good pain management because pain can only be understood from the patient's perspective. We aimed to develop a user-centered tool to improve patient-provider communication about chronic pain and assess its feasibility in real-world settings in preparation for further evaluation and distribution.MethodsTo identify and prioritize patient treatment goals for chronic pain, strategies to improve patient-provider communication about chronic pain, and facilitate implementation of the tool, we conducted nominal group technique meetings and card sorting with patients with chronic pain and experienced providers (n = 12). These findings informed the design of the PainAPP tool. Usability and beta-testing with patients (n = 38) and their providers refined the tool and assessed its feasibility, acceptability, and preliminary impact.ResultsFormative work revealed that patients felt neither respected nor trusted by their providers and focused on transforming providers' negative attitudes towards them, whereas providers focused on gathering patient information. PainAPP incorporated areas prioritized by patients and providers: assessing patient treatment goals and preferences, functional abilities and pain, and providing patients tailored education and an overall summary that patients can share with providers. Beta-testing involved 38 patients and their providers. Half of PainAPP users shared their summaries with their providers. Patients rated PainAPP highly in all areas. All users would recommend it to others with chronic pain; nearly all trusted the information and said it helped them think about my treatment goals (94%), understand my chronic pain (82%), make the most of my next doctor's visit (82%), and not want to use opioids (73%). Beta-testing revealed challenges delivering the tool and summary report to patients and providers in a timely manner and obtaining provider feedback.ConclusionsPainAPP appears feasible for use, but further adaptation and testing is needed to assess its impact on patients and providers.Trial registrationThis study was approved by the University of New England Independent Review Board for the Protection of Human Subjects in Research (012616-019) and was registered with ClinicalTrials.gov (protocol ID: NCT03425266) prior to enrollment. The trial was prospectively registered and was approved on February 7, 2018.
| S-EPMC7568350 | biostudies-literature
Project description:PurposeMany hospitalized patients experience barriers to effective patient-provider communication that can negatively impact their care. These barriers include difficulty physically accessing the nurse call system, communicating about pain and other needs, or both. For many patients, these barriers are a result of their admitting condition and not of an underlying chronic disability. Speech-language pathologists have begun to address patients' short-term communication needs with an array of augmentative and alternative communication (AAC) strategies.MethodThis study used a between-groups experimental design to evaluate the impact of providing patients with AAC systems so that they could summon help and communicate with their nurses. The study examined patients' and nurses' perceptions of the patients' ability to summon help and effectively communicate with caregivers.ResultsPatients who could summon their nurses and effectively communicate-with or without AAC-had significantly more favorable perceptions than those who could not.ConclusionsThis study suggests that AAC can be successfully used in acute care settings to help patients overcome access and communication barriers. Working with other members of the health care team is essential to building a "culture of communication" in acute care settings.Supplemental materialhttps://doi.org/10.23641/asha.9990962.
| S-EPMC8188878 | biostudies-literature
Project description:ObjectiveWe developed, tested, and validated machine learning algorithms to predict individual patient-reported outcomes at 1-year follow-up to facilitate individualized, patient-centered decision-making for women with breast cancer.Summary of background dataSatisfaction with breasts is a key outcome for women undergoing cancer-related mastectomy and reconstruction. Current decision-making relies on group-level evidence which may lead to suboptimal treatment recommendations for individuals.MethodsWe trained, tested, and validated 3 machine learning algorithms using data from 1921 women undergoing cancer-related mastectomy and reconstruction conducted at eleven study sites in North America from 2011 to 2016. Data from 1921 women undergoing cancer-related mastectomy and reconstruction were collected before surgery and at 1-year follow-up. Data from 10 of the 11 sites were randomly split into training and test samples (2:1 ratio) to develop and test 3 algorithms (logistic regression with elastic net penalty, extreme gradient boosting tree, and neural network) which were further validated using the additional site's data.AUC to predict clinically-significant changes in satisfaction with breasts at 1-year follow-up using the validated BREAST-Q were the outcome measures.ResultsThe 3 algorithms performed equally well when predicting both improved or decreased satisfaction with breasts in both testing and validation datasets: For the testing dataset median accuracy = 0.81 (range 0.73-0.83), median AUC = 0.84 (range 0.78-0.85). For the validation dataset median accuracy = 0.83 (range 0.81-0.84), median AUC = 0.86 (range 0.83-0.89).ConclusionIndividual patient-reported outcomes can be accurately predicted using machine learning algorithms, which may facilitate individualized, patient-centered decision-making for women undergoing breast cancer treatment.
| S-EPMC9762704 | biostudies-literature
Project description:Online decision-making problem requires us to make a sequence of decisions based on incremental information. Common solutions often need to learn a reward model of different actions given the contextual information and then maximize the long-term reward. It is meaningful to know if the posited model is reasonable and how the model performs in the asymptotic sense. We study this problem under the setup of the contextual bandit framework with a linear reward model. The ε-greedy policy is adopted to address the classic exploration-and-exploitation dilemma. Using the martingale central limit theorem, we show that the online ordinary least squares estimator of model parameters is asymptotically normal. When the linear model is misspecified, we propose the online weighted least squares estimator using the inverse propensity score weighting and also establish its asymptotic normality. Based on the properties of the parameter estimators, we further show that the in-sample inverse propensity weighted value estimator is asymptotically normal. We illustrate our results using simulations and an application to a news article recommendation dataset from Yahoo!.
| S-EPMC7962379 | biostudies-literature
Project description:Purpose: This study was conducted to determine the level of knowledge, attitude, and practice of oral health care providers toward the use of online medical databases for clinical decision-making processes. Subjects and methods: The study population included all the licensed oral health care providers living in Rwanda, registered either with the Rwanda Allied Health Professional Council (RAHPC) or Rwanda Medical and Dental Council (RMDC). A self-administered questionnaire was used to collect demographic data as well as data regarding knowledge, attitude, and practice of oral health care providers regarding the use of online medical databases for making clinical decisions. A pilot study with 12 oral health care providers was done before the main study to pretest the questionnaire. Results: The study results show that among the 201 respondents, 80% (N=160) reported using internet-based resources to support their clinical decisions, while 20% (N=41) of oral health care providers do not use online resources when making their clinical decisions. In general, there was a positive attitude towards internet-based resources among the participants, as 92% (N=184) respondents believe that internet-based resources are helpful in clinical decision-making processes. Of clinical importance, 68% respondents (N=136) believe that by using current internet-based information, better clinical care can be offered to their patients. Conclusion: Educating oral health care providers on the useful and appropriate online resources available for supporting clinical decision-making processes might increase the efficiency of patient care.
| S-EPMC6600754 | biostudies-literature
Project description:BackgroundSystematic reviews allow health decisions to be informed by the best available research evidence. However, their number is proliferating quickly, and many skills are required to identify all the relevant reviews for a specific question.Methods and findingsWe screen 10 bibliographic databases on a daily or weekly basis, to identify systematic reviews relevant for health decision-making. Using a machine-based approach developed for this project we select reviews, which are then validated by a network of more than 1000 collaborators. After screening over 1,400,000 records we have identified more than 300,000 systematic reviews, which are now stored in a single place and accessible through an easy-to-use search engine. This makes Epistemonikos the largest database of its kind.ConclusionsUsing a systematic approach, recruiting a broad network of collaborators and implementing automated methods, we developed a one-stop shop for systematic reviews relevant for health decision making.
| S-EPMC7708132 | biostudies-literature