Background:
The significant role of lay caregivers has been explored in chronic and acute illnesses. In pregnancy, caregivers’ (eg, the baby’s father, friends, and family) roles in promoting the ...health of the mother and baby are not well understood.
Objective:
We characterize the activities and roles of pregnancy caregivers and offer opportunities for engaging this important group.
Method:
We conducted interviews with 29 pregnancy caregivers. Interview transcripts were analyzed inductively, resulting in a coding scheme of actions and roles that pregnancy caregivers perform.
Results:
The most common actions and roles included searching for information (97%), accompanying patients to medical appointments (69%), and being a source of emotional support (76%). Identified actions and roles fit a patient work framework, including work types identified by Corbin and Strauss: illness, everyday life, biographical, articulation, and invisible.
Conclusion:
The patient work framework can be employed to describe the activities and roles of pregnancy caregivers. We have contributed new insights into the experiences of pregnancy caregivers and recommendations for educational and technological interventions.
To examine the impact of a clinical decision support system (CDSS) on breast cancer treatment decisions and adherence to National Comprehensive Cancer Center (NCCN) guidelines.
A cross-sectional ...observational study was conducted involving 1,977 patients at high risk for recurrent or metastatic breast cancer from the Chinese Society of Clinical Oncology. Ten oncologists provided blinded treatment recommendations for an average of 198 patients before and after viewing therapeutic options offered by the CDSS. Univariable and bivariable analyses of treatment changes were performed, and multivariable logistic regressions were estimated to examine the effects of physician experience (years), patient age, and receptor subtype/TNM stage.
Treatment decisions changed in 105 (5%) of 1,977 patients and were concentrated in those with hormone receptor (HR)-positive disease or stage IV disease in the first-line therapy setting (73% and 58%, respectively). Logistic regressions showed that decision changes were more likely in those with HR-positive cancer (odds ratio OR, 1.58;
< .05) and less likely in those with stage IIA (OR, 0.29;
< .05) or IIIA cancer (OR, 0.08;
< .01). Reasons cited for changes included consideration of the CDSS therapeutic options (63% of patients), patient factors highlighted by the tool (23%), and the decision logic of the tool (13%). Patient age and oncologist experience were not associated with decision changes. Adherence to NCCN treatment guidelines increased slightly after using the CDSS (0.5%;
= .003).
Use of an artificial intelligence-based CDSS had a significant impact on treatment decisions and NCCN guideline adherence in HR-positive breast cancers. Although cases of stage IV disease in the first-line therapy setting were also more likely to be changed, the effect was not statistically significant (
= .22). Additional research on decision impact, patient-physician communication, learning, and clinical outcomes is needed to establish the overall value of the technology.
Palliative care and pediatric surgery Shelton, Julia; Jackson, Gretchen Purcell
The Surgical clinics of North America,
04/2011, Volume:
91, Issue:
2
Journal Article
Peer reviewed
Pediatric surgeons can play an important role in offering procedures that may improve the quality of life for terminally ill children. As with all palliative interventions, surgical therapies should ...be evaluated in the context of explicitly defined treatment goals while weighing the risks and benefits of procedures in the context of a shortened life span. It is essential that pediatric surgeons become active members in the multidisciplinary team that provides palliative care.
Vanderbilt University has a widely adopted patient portal, MyHealthAtVanderbilt, which provides an infrastructure to deliver information that can empower patient decision making and enhance ...personalized healthcare. An interdisciplinary team has developed Flu Tool, a decision-support application targeted to patients with influenza-like illness and designed to be integrated into a patient portal. Flu Tool enables patients to make informed decisions about the level of care they require and guides them to seek timely treatment as appropriate. A pilot version of Flu Tool was deployed for a 9-week period during the 2010-2011 influenza season. During this time, Flu Tool was accessed 4040 times, and 1017 individual patients seen in the institution were diagnosed as having influenza. This early experience with Flu Tool suggests that healthcare consumers are willing to use patient-targeted decision support. The design, implementation, and lessons learned from the pilot release of Flu Tool are described as guidance for institutions implementing decision support through a patient portal infrastructure.
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•Extreme preterm birth (EPB) accounts for the majority of newborn deaths.•Deep learning models that consider temporal relations can predict EPB.•Deep learning ensemble models achieve ...a higher performance than individual models.•EPB is associated with significant morbidity, e.g., systemic lupus erythematosus.
Models for predicting preterm birth generally have focused on very preterm (28–32 weeks) and moderate to late preterm (32–37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the majority of newborn deaths. We investigated the extent to which deep learning models that consider temporal relations documented in electronic health records (EHRs) can predict EPB.
EHR data were subject to word embedding and a temporal deep learning model, in the form of recurrent neural networks (RNNs) to predict EPB. Due to the low prevalence of EPB, the models were trained on datasets where controls were undersampled to balance the case-control ratio. We then applied an ensemble approach to group the trained models to predict EPB in an evaluation setting with a nature EPB ratio. We evaluated the RNN ensemble models with 10 years of EHR data from 25,689 deliveries at Vanderbilt University Medical Center. We compared their performance with traditional machine learning models (logistical regression, support vector machine, gradient boosting) trained on the datasets with balanced and natural EPB ratio. Risk factors associated with EPB were identified using an adjusted odds ratio.
The RNN ensemble models trained on artificially balanced data achieved a higher AUC (0.827 vs. 0.744) and sensitivity (0.965 vs. 0.682) than those RNN models trained on the datasets with naturally imbalanced EPB ratio. In addition, the AUC (0.827) and sensitivity (0.965) of the RNN ensemble models were better than the AUC (0.777) and sensitivity (0.819) of the best baseline models trained on balanced data. Also, risk factors, including twin pregnancy, short cervical length, hypertensive disorder, systemic lupus erythematosus, and hydroxychloroquine sulfate, were found to be associated with EPB at a significant level.
Temporal deep learning can predict EPB up to 8 weeks earlier than its occurrence. Accurate prediction of EPB may allow healthcare organizations to allocate resources effectively and ensure patients receive appropriate care.
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Background: In the US, the incidence of colorectal cancer (CRC) is increasing in patients younger than 50 years who may present with advanced stage, high grade, left-sided colon or ...rectal cancers with signet ring cell histopathology, aggressive clinical course, and reduced overall survival. Understanding the characteristics of this population could inform screening, early detection, and optimal treatment. In this study, we describe the attributes of adults who are 50 years and younger with a first diagnosis of CRC and ascertain molecular testing rates and time to surgery by using data from a commercially insured cohort in the U.S. Methods: This retrospective study of patients ages 50 and younger with a first diagnosis of CRC utilizes the IBM MarketScan database, and focuses on claims from January 2013 to December 2018. Included patients had continuous insurance enrollment of 12 months before and 6 months after diagnosis. We determined rates of tumor testing for microsatellite instability (MSI) or immunohistochemistry (IHC) for mismatch repair (MMR) proteins and referral to genetic services in all patients, as well as mutational analysis of KRAS, NRAS, and BRAF in metastatic CRC patients. Time to surgical resection of primary tumor (TTS) in non-metastatic colon cancer patients was measured. Results: During the 5-year period, 10,577 patients ages 18 to 50 years had a first diagnosis of CRC, which was 15.6% of the 67,921 adults of all ages with CRC. Claims for MSI or IHC for MMR proteins within 120 days of initial diagnosis were done in 4,429 (41.9%) patients and referral to genetics services/counseling within 1 year of initial diagnosis were done in 443 (4.1%) patients. Among metastatic CRC patients, KRAS, NRAS, or BRAF tumor mutational analyses within 120 days of initial diagnosis were documented in 323 (31.5%). The median TTS ranged from 7 to 15 days with no statistically significant differences based on geographic region or health insurance plan type. Conclusions: Younger patients with early onset CRC had low rates of referral to genetics services, tumor MSI or IHC for MMR proteins testing, and KRAS, NRAS, and BRAF mutational analysis. There were no geographic or insurance type trends in TTS in non-metastatic colon cancer patients. Although underreporting is possible in our study, the findings of low utilization of genetic services and tumor genomic testing in these younger patients with early onset CRC should alert the oncology community to critical management gaps in the care of this population.
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120
Background: Targeted therapies are superior to chemotherapy in metastatic lung cancer with driver gene mutations. Delays in initiation of targeted therapies may result in faster ...symptom progression, decline in quality of life, and shortened survival. We examined factors associated with time to initiation of targeted therapy (TTT) in patients with metastatic lung cancer with selected driver mutations. Methods: In this retrospective cohort study, IBM MarketScan claims data was used to identify patients who had an initial diagnosis of metastatic lung cancer, defined as continuous insurance enrollment 12 months pre- to 6 months post-diagnosis, with tumor biomarker (i.e., EGFR, ALK, ROS1, BRAF V600E, NTRK)-directed targeted therapy performed within 6 months of the initial diagnosis, during the timeframe of 1/1/2013 to 12/31/2018. Trends in TTT were evaluated with Wilcoxon–Mann–Whitney. Quantile regression, a robust model that analyzed factors on different outcome-related quantiles, was used to identify associations among TTT and covariates including age, sex, comorbidity, insurance type, and US region. Results: Among 8977 patients identified with an initial diagnosis of metastatic lung cancer, 710 (7.9%) received targeted therapies within the 6-month timeframe, and 1040 (12%) had tumor biomarker testing performed. The overall median TTT was 21 days (IQR = 36 days). Median TTT decreased from 25 days in 2013 to 18 days in 2018 (p = 0.03). Factors associated with longer TTT (median, 50% quantile) were increasing age (p = 0.04), cardiovascular disease (“CVD”, p = 0.03), HIV (p = 0.04), and mild liver disease (p = 0.05). For the lower quantile ( < = 1 day, 5% quantile), female sex (p = 0.01), HIV (p = 0.04), and mild liver disease (p = 0.002) were associated with longer TTT. Having a PPO health plan extended TTT (p = 0.05) at the upper quantile (79 days, 90% quantile). Conclusions: Our study showed an encouraging 5-year trend of the median TTT decreasing by 28%. Numerous factors associated with longer TTT included increasing age, CVD, HIV, mild liver disease, female sex, and PPO plan. This study provides insights into patient-related factors associated with longer time to initiation of targeted therapies for patients with metastatic lung cancer with driver mutations. Additional research is needed to identify the reasons for longer TTT and to develop strategies to expedite delivery of optimal therapies.
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•Standard text classifiers are unable to capture clinical communication semantics.•Word embeddings, such as word2vec, are able to extract term relationships.•Convolutional neural ...networks (CNNs) can generate higher-order features for text.•Classifiers using CNNs and word embeddings improve communication classification.•The enhanced classifier outperforms standard methods by 1.5–10%.
Patients communicate with healthcare providers via secure messaging in patient portals. As patient portal adoption increases, growing messaging volumes may overwhelm providers. Prior research has demonstrated promise in automating classification of patient portal messages into communication types to support message triage or answering. This paper examines if using semantic features and word context improves portal message classification.
Portal messages were classified into the following categories: informational, medical, social, and logistical. We constructed features from portal messages including bag of words, bag of phrases, graph representations, and word embeddings. We trained one-versus-all random forest and logistic regression classifiers, and convolutional neural network (CNN) with a softmax output. We evaluated each classifier’s performance using Area Under the Curve (AUC).
Representing the messages using bag of words, the random forest detected informational, medical, social, and logistical communications in patient portal messages with AUCs: 0.803, 0.884, 0.828, and 0.928, respectively. Graph representations of messages outperformed simpler features with AUCs: 0.837, 0.914, 0.846, 0.884 for informational, medical, social, and logistical communication, respectively. Representing words with Word2Vec embeddings, and mapping features using a CNN had the best performance with AUCs: 0.908 for informational, 0.917 for medical, 0.935 for social, and 0.943 for logistical categories.
Word2Vec and graph representations improved the accuracy of classifying portal messages compared to features that lacked semantic information such as bag of words, and bag of phrases. Furthermore, using Word2Vec along with a CNN model, which provide a higher order representation, improved the classification of portal messages.
Advances in informatics research come from academic, nonprofit, and for-profit industry organizations, and from academic-industry partnerships. While scientific studies of commercial products may ...offer critical lessons for the field, manuscripts authored by industry scientists are sometimes categorically rejected. We review historical context, community perceptions, and guidelines on informatics authorship.
We convened an expert panel at the American Medical Informatics Association 2022 Annual Symposium to explore the role of industry in informatics research and authorship with community input. The panel summarized session themes and prepared recommendations.
Authorship for informatics research, regardless of affiliation, should be determined by International Committee of Medical Journal Editors uniform requirements for authorship. All authors meeting criteria should be included, and categorical rejection based on author affiliation is unethical. Informatics research should be evaluated based on its scientific rigor; all sources of bias and conflicts of interest should be addressed through disclosure and, when possible, methodological mitigation.
Abstract only
e13667
Background: Guidelines for biomarker testing of metastatic lung cancer patients aid oncologists in making targeted treatment decisions. Despite evidence demonstrating the ...benefits of genomic and immune biomarker identification in these patients, variations in testing exist. This population-based, retrospective, observational study examined trends in testing rates and timing, assessing associations between testing and patient characteristics, sociodemographic factors, and regional patterns using insurance claims data. Methods: We evaluated patterns of biomarker testing in the IBM MarketScan database between 1/1/2013-12/31/2018. Inclusion criteria consisted of lung cancer patients with an initial diagnosis of metastasis within the study period, continuous insurance coverage from 12 months before to 4 months post-diagnosis, and biomarker testing (EGFR, ALK, ROS1, BRAF V600E, NTRK, PD-L1) within 4 months of diagnosis. Temporal trends were evaluated by the Cochran-Armitage method. Multivariate logistic regression evaluated associations between testing rates and patient-specific factors (i.e., age, gender, comorbid conditions), insurance type, and region (i.e., Northeastern, North central, Southern, and Western) in the United States (US). Results: Of the 8977 patients with metastatic lung cancer, 1040 (12%) had claims for biomarker testing. During the study period, testing rates increased significantly, from 8.4% in 2013 to 20.6% in 2018 (P <.0001); the likelihood of testing increased by year (2014, OR 1.20, 95% CI 0.97 - 1.48 vs. 2018, OR 2.83, 95% CI 2.26 - 3.54). Of patients tested, 25.8% (N = 268) were tested on the day of diagnosis, 70.7 % (N = 735) within 30 days, and 85.6% (N = 890) within 60 days. A lower likelihood of testing was associated with increasing age (OR = 0.97, 95% CI 0.96 - 0.98), enrollment in preferred provider health plans (OR 0.69, 95% CI 0.53 – 0.93), or pre-existing comorbidities of congestive heart failure (OR 0.76, 95% CI 0.59 – 0.98) or diabetes (OR 0.82, 95% CI 0.68 – 0.99). Testing was more likely to occur in females (OR 1.24, 95% CI 1.09 – 1.42), age < 55 years (OR 1.67, 95% CI 1.32 – 2.12) or residence in Northeastern US (OR 1.26, 95% CI 1.05 -1.51). Conclusions: Biomarker testing rates for an insured cohort of metastatic lung cancer patients increased significantly over time, but the likelihood of testing varied based on age, sex, insurance type, comorbidities, and region. Results of this study may inform policy or outreach strategies by highlighting population-based factors influencing biomarker testing rates.
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