Background
Decision aids are intended to help people participate in decisions that involve weighing the benefits and harms of treatment options often with scientific uncertainty.

Objectives
To assess the effects of decision aids for people facing treatment or screening decisions.

Search methods
For this update, we searched from 2009 to June 2012 in MEDLINE; CENTRAL; EMBASE; PsycINFO; and grey literature. Cumulatively, we have searched each database since its start date including CINAHL (to September 2008).

Selection criteria
We included published randomized controlled trials of decision aids, which are interventions designed to support patients' decision making by making explicit the decision, providing information about treatment or screening options and their associated outcomes, compared to usual care and/or alternative interventions. We excluded studies of participants making hypothetical decisions.

Data collection and analysis
Two review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. The primary outcomes, based on the International Patient Decision Aid Standards (IPDAS), were:

A) 'choice made' attributes;

B) 'decision-making process' attributes.

Secondary outcomes were behavioral, health, and health-system effects. We pooled results using mean differences (MD) and relative risks (RR), applying a random-effects model.

Main results
This update includes 33 new studies for a total of 115 studies involving 34,444 participants. For risk of bias, selective outcome reporting and blinding of participants and personnel were mostly rated as unclear due to inadequate reporting. Based on 7 items, 8 of 115 studies had high risk of bias for 1 or 2 items each.

Of 115 included studies, 88 (76.5%) used at least one of the IPDAS effectiveness criteria: A) 'choice made' attributes criteria: knowledge scores (76 studies); accurate risk perceptions (25 studies); and informed value-based choice (20 studies); and B) 'decision-making process' attributes criteria: feeling informed (34 studies) and feeling clear about values (29 studies).

A) Criteria involving 'choice made' attributes:

Compared to usual care, decision aids increased knowledge (MD 13.34 out of 100; 95% confidence interval (CI) 11.17 to 15.51; n = 42). When more detailed decision aids were compared to simple decision aids, the relative improvement in knowledge was significant (MD 5.52 out of 100; 95% CI 3.90 to 7.15; n = 19). Exposure to a decision aid with expressed probabilities resulted in a higher proportion of people with accurate risk perceptions (RR 1.82; 95% CI 1.52 to 2.16; n = 19). Exposure to a decision aid with explicit values clarification resulted in a higher proportion of patients choosing an option congruent with their values (RR 1.51; 95% CI 1.17 to 1.96; n = 13).

B) Criteria involving 'decision-making process' attributes:

Decision aids compared to usual care interventions resulted in:

a) lower decisional conflict related to feeling uninformed (MD -7.26 of 100; 95% CI -9.73 to -4.78; n = 22) and feeling unclear about personal values (MD -6.09; 95% CI -8.50 to -3.67; n = 18);

b) reduced proportions of people who were passive in decision making (RR 0.66; 95% CI 0.53 to 0.81; n = 14); and

c) reduced proportions of people who remained undecided post-intervention (RR 0.59; 95% CI 0.47 to 0.72; n = 18).

Decision aids appeared to have a positive effect on patient-practitioner communication in all nine studies that measured this outcome. For satisfaction with the decision (n = 20), decision-making process (n = 17), and/or preparation for decision making (n = 3), those exposed to a decision aid were either more satisfied, or there was no difference between the decision aid versus comparison interventions. No studies evaluated decision-making process attributes for helping patients to recognize that a decision needs to be made, or understanding that values affect the choice.

C) Secondary outcomes

Exposure to decision aids compared to usual care reduced the number of people of choosing major elective invasive surgery in favour of more conservative options (RR 0.79; 95% CI 0.68 to 0.93; n = 15). Exposure to decision aids compared to usual care reduced the number of people choosing to have prostate-specific antigen screening (RR 0.87; 95% CI 0.77 to 0.98; n = 9). When detailed compared to simple decision aids were used, fewer people chose menopausal hormone therapy (RR 0.73; 95% CI 0.55 to 0.98; n = 3). For other decisions, the effect on choices was variable.

The effect of decision aids on length of consultation varied from 8 minutes shorter to 23 minutes longer (median 2.55 minutes longer) with 2 studies indicating statistically-significantly longer, 1 study shorter, and 6 studies reporting no difference in consultation length. Groups of patients receiving decision aids do not appear to differ from comparison groups in terms of anxiety (n = 30), general health outcomes (n = 11), and condition-specific health outcomes (n = 11). The effects of decision aids on other outcomes (adherence to the decision, costs/resource use) were inconclusive.

Authors' conclusions
There is high-quality evidence that decision aids compared to usual care improve people's knowledge regarding options, and reduce their decisional conflict related to feeling uninformed and unclear about their personal values. There is moderate-quality evidence that decision aids compared to usual care stimulate people to take a more active role in decision making, and improve accurate risk perceptions when probabilities are included in decision aids, compared to not being included. There is low-quality evidence that decision aids improve congruence between the chosen option and the patient's values.

New for this updated review is further evidence indicating more informed, values-based choices, and improved patient-practitioner communication. There is a variable effect of decision aids on length of consultation. Consistent with findings from the previous review, decision aids have a variable effect on choices. They reduce the number of people choosing discretionary surgery and have no apparent adverse effects on health outcomes or satisfaction. The effects on adherence with the chosen option, cost-effectiveness, use with lower literacy populations, and level of detail needed in decision aids need further evaluation. Little is known about the degree of detail that decision aids need in order to have a positive effect on attributes of the choice made, or the decision-making process.

/pdf/decision-aids-for-people-facing-health-treatment-or-1j1706q17l.pdf

Decision aids for people facing health treatment or screening decisions

5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding

The adoption of electronic health records will provide a rich resource for biomedical researchers. This Review discusses the potential for their use in informed decision making in the clinic, for a finer understanding of genotype–phenotype relationships and for selection of research cohorts, along with the current challenges for their mining and use. Clinical data describing the phenotypes and treatment of patients represents an underused data source that has much greater research potential than is currently realized. Mining of electronic health records (EHRs) has the potential for establishing new patient-stratification principles and for revealing unknown disease correlations. Integrating EHR data with genetic data will also give a finer understanding of genotype–phenotype relationships. However, a broad range of ethical, legal and technical reasons currently hinder the systematic deposition of these data in EHRs and their mining. Here, we consider the potential for furthering medical research and clinical care using EHR data and the challenges that must be overcome before this is a reality.

Mining electronic health records: towards better research applications and clinical care

The AUDIT Alcohol Consumption Questions (AUDIT-C) An Effective Brief Screening Test for Problem Drinking

Caroline Free and colleagues systematically review controlled trials of mobile technology interventions to improve health care delivery processes and show that current interventions give only modest benefits and that high-quality trials measuring clinical outcomes are needed.

/pdf/the-effectiveness-of-mobile-health-technologies-to-improve-1pj8i7l0ez.pdf

The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis.

A systematic and standard process for capturing information within free-text clinical documents could facilitate opportunities for improving quality and safety of patient care, enhancing decision support, and advancing data warehousing across an enterprise setting. At Partners HealthCare System, the Medical Language Processing (MLP) services project was initiated to establish a component-based architectural model and processes to facilitate putting MLP functionality into production for enterprise consumption, promote sharing of components, and encourage reuse. Key objectives included exploring the use of an open-source framework called the Unstructured Information Management Architecture (UIMA) and leveraging existing MLP-related efforts, terminology, and document standards. This paper describes early experiences in defining the infrastructure and standards for extracting, encoding, and structuring clinical observations from a variety of clinical documents to serve enterprise-wide needs.

Laying the groundwork for enterprise-wide medical language processing services: architecture and process.

We are building an extension of the existing Web-based clinical information system at Columbia-Presbyterian Medical Center (CPMC) that enables providers to communicate in a virtual environment. We call it a Virtual Whiteboard1. We intend to export the features of this application to a wireless PDA (PalmCIS). One of the elements required for this system is accurate information about which providers are covering each patient at any given time. This paper outlines our effort to build a web-based interface that captures the schedule information of clinicians. The application will automatically provide this information for display in the Virtual Whiteboard.

Design of a Web-based Care Team Scheduler for PalmCIS

Diagnostic errors can pose a serious threat to patient safety, leading to serious harm and even death. Efforts are being made to develop interventions that allow physicians to reassess for errors and improve diagnostic accuracy. Our study presents an exploration of misdiagnosis patterns mined from PubMed abstracts. Article titles containing certain phrases indicating misdiagnosis were selected and frequencies of these misdiagnoses calculated. We present the resulting patterns in the form of a directed graph with frequency-weighted misdiagnosis edges connecting diagnosis vertices. We find that the most commonly misdiagnosed diseases were often misdiagnosed as many different diseases, with each misdiagnosis having a relatively low frequency, rather than as a single disease with greater probability. Additionally, while a misdiagnosis relationship may generally exist, the relationship was often found to be one-sided.

Mining Misdiagnosis Patterns from Biomedical Literature.

Data, Text, and Web Mining in Healthcare

Many recent studies use machine learning to predict a small number of ICD-9-CM codes In practice, on the other hand, physicians have to consider a broader range of diagnoses This study aims to put these previously incongruent evaluation settings on a more equal footing by predicting ICD-9-CM codes based on electronic health record properties and demonstrating the relationship between diagnosis prevalence and system performance We extracted patient features from the MIMIC-III dataset for each admission We trained and evaluated 43 different machine learning classifiers Among this pool, the most successful classifier was a Multi-Layer Perceptron In accordance with general machine learning expectation, we observed all classifiers' F1 scores to drop as disease prevalence decreased Scores fell from 028 for the 50 most prevalent ICD-9-CM codes to 003 for the 1000 most prevalent ICD-9-CM codes Statistical analyses showed a moderate positive correlation between disease prevalence and efficacy (05866)

Elizabeth S. Chen

Papers

Laying the groundwork for enterprise-wide medical language processing services: architecture and process.

Design of a Web-based Care Team Scheduler for PalmCIS

Mining Misdiagnosis Patterns from Biomedical Literature.

Data, Text, and Web Mining in Healthcare

Diagnosis Prevalence vs. Efficacy in Machine-learning Based Diagnostic Decision Support