Patient decision aids: a content analysis based on a decision tree structure

Development of a web-based patient decision aid for myopia laser correction method

BACKGROUND

In the context of healthcare centered on the patient, Patient Decision Aids (PtDAs) acts as an essential instrument, promoting shared decision-making (SDM). Considering the prevalent occurrence of myopia, the objective of this study is to furnish exhaustive and easily comprehensible information to assist patients in making well-informed decisions about their options for myopia laser correction.

METHOD

The research team developed a decision guide for myopia patients considering laser correction, aiming to facilitate informed decisions. The study followed the first four stages of the IPDAS process model: "scope/scoping," "design," "prototype development," and "alpha testing." Ten semi-structured interviews with patients (n = 6) and corneal specialist ophthalmologists (n = 4) were conducted to understand the challenges in selecting a laser correction method. Online meetings with 4 corneal specialists were held to discuss challenging cases. A comparison table of harms and benefits was created. The initial prototype was developed and uploaded on the internet portal. User feedback on software and text aspects was incorporated into the final web software, which was reviewed by a health education expert for user-friendliness and effectiveness.

RESULT

Educational needs assessment revealed concerns such as pain, daily life activities, return to work, the potential need for glasses ('number return'), eye prescription stability, and possible complications. These shaped the decision aid tool's content. Expert consensus was achieved in several areas, with some items added or extended. In areas lacking consensus, comments were added for clarity. Five clients assessed the web app (PDAIN), rating it 46/50 in user-centricity, 47/50 in usability, and 45/50 in accuracy and reliability, totaling 138/150. Post-piloting, software errors were documented and rectified. During the trial phase, five myopic users interacted with the software, leading to modifications. User feedback indicated the tool effectively enhanced understanding and influenced decision-making.

CONCLUSION

PDAIN, serves as a facilitative tool in the process of selecting a corneal laser correction method for myopic patients. It enabling Nearsighted patients to make informed decisions.

Multilayer framework for digital multicomponent platform design for colorectal survivors and carers: a qualitative study

Background

The advent of eHealth services offers the potential to support colorectal cancer (CRC) survivors and their informal caregivers (ICs), yet research into user needs and design requirements remains scant. This exploratory qualitative study addresses this knowledge gap by focusing on the development of a Digital Multicomponent Platform (DMP) designed to provide comprehensive support to these populations.

Aims

The objective of this research is to use qualitative methodologies to identify key user needs and design requirements for eHealth services. It seeks to propose and apply a multi-tiered framework for creating a DMP that encapsulates the needs of CRC survivors and their ICs.

Methods

Skype-based focus groups (FGs) were utilized to gather qualitative data from CRC survivors and ICs. This approach served to elicit crucial themes integral to the design of the DMP. A multi-tiered framework was subsequently developed to integrate user-centered design (UCD) principles and requirements with predetermined outcomes, eHealth services, and IT infrastructure.

Results

The first stage of the analysis identified five crucial themes: (1) the importance of healthcare system interaction via eHealth, (2) interaction between healthcare providers and peers, (3) lifestyle and wellness considerations, (4) platform content and user interface requirements, (5) caregiver support. The second stage analysis applied the multi-tiered framework, to determine the DMP that was conceptualized from these themes, underscores the significance of personalized content, caregiver involvement, and integration with electronic health records (EHRs).

Conclusion

The study offers novel insights into the design and development of digital supportive care interventions for CRC survivors and their caregivers. The results highlight the utility of user-centered design principles, the significance of personalized content and caregiver involvement, and the need for a unified health data platform that promotes communication among patients, healthcare providers, and peers. This multi-tiered framework could serve as a prototype for future eHealth service designs.

Basic psychological needs satisfaction of stroke patients: a qualitative study

BACKGROUND

Previous studies have shown that the satisfaction of basic psychological needs is related to psychological well-being. Improving satisfaction will increase personal well-being, promote positive health outcomes, and improve disease recovery. However, no research has focused on the basic psychological needs of stroke patients. Therefore, this study aims to determine the basic psychological needs experience, satisfaction, and its influencing factors of stroke patients.

METHODS

12 males and 6 females in the non-acute phase with stroke were recruited in the Department of Neurology, Nanfang Hospital. The individual, semi-structured interviews were conducted in a separate room. The data were imported to Nvivo 12 and analyzed using the directed content analysis approach.

RESULTS

Three main themes consisting of 9 sub-themes were derived from the analysis. These three main themes focused on the needs for autonomy, competence, and relatedness of stroke patients.

CONCLUSION

Participants have different degrees of satisfaction of their basic psychological needs, which may be related to their family environment, work environment, stroke symptoms, or other factors. Stroke symptoms can significantly reduce the patients' needs for autonomy and competence. However, the stroke seems to increase the patients' satisfaction of the need for relatedness.

Choosing between the red and the blue pill. How do people decide when they face uncertainty regarding different treatment alternatives?

BACKGROUND

When we are faced with health challenges, we have to choose a treatment from several alternatives. Most of the time, we must make a choice even though some information regarding the options is missing. Previous research found that missing information systematically impacts our choices.

AIM

The present study investigated if context-related variables (type of information: advantages or costs, the label of the alternatives) and individual differences (moral purity, thinking style) have an impact on the way people make these kinds of choices. Methods: One hundred twenty-three students (52% males) had to make 27 decisions regarding their preferred alternative for treating various medical conditions. We manipulated the type of comparable information (i.e., regarding advantages, disadvantages, or costs), and the label of the treatment alternatives (i.e., abstract vs. recognizabletreatments). Additionally, we measured the participants' moral purity endorsement and thinking style via self-report questionnaires.

RESULTS

The results showed that context variables like the type of comparable information and the label of the alternatives are significant predictors of people's medical treatment choices. At the same time, self-reported measures were unrelated to the way people choose medical treatment.

CONCLUSION

The results highlight the importance of discussing the issue of missing information with healthcare consumers and patients.

Health Literacy and eHealth: Challenges and Strategies

Given the impact of health literacy (HL) on patients' outcomes, limited health literacy is a major barrier to improve cancer care globally. HL refers to the degree in which an individual is able to acquire, process, and comprehend information in a way to be actively involved in their health decisions. Previous research found that almost half of the population in developed countries have difficulties in understanding health-related information. With the gradual shift toward the shared decision making process and digital transformation in oncology, the need for addressing low HL issues is crucial. Decision making in oncology is often accompanied by considerable consequences on patients' lives, which requires patients to understand complex information and be able to compare treatment methods by considering their own values. How health information is perceived by patients is influenced by various factors including patients' characteristics and the way information is presented to patients. Currently, identifying patients with low HL and simple data visualizations are the best practice to help patients and clinicians in dealing with limited health literacy. Furthermore, using eHealth, as well as involving HL mediators, supports patients to make sense of complex information.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Effects of patient decision aids in patients with type 2 diabetes mellitus: A systematic review and meta-analysis

AIMS

This study aimed to systematically evaluate the effectiveness of patient decision aids on knowledge, decisional conflict and decisional self-efficacy outcomes in patients with diabetes.

METHODS

A comprehensive database search was performed using the Web of Science, Cochrane Library, PubMed, Embase, PsycINFO (Ovid), CINAHL (EBASCO), CNKI, VIP, Wan Fang Database and the Ottawa Decision Aid Library Inventory (http://decisionaid.ohri.ca/index.html) from inception to 13 October 2019. Two reviewers independently searched databases, screened articles, extracted data and evaluated the risk bias of included studies. Then Rev Man 5.3 software was adopted for statistical analysis.

RESULTS

Ten articles containing 1,452 people with diabetes were selected. The results of meta-analysis showed that patient decision aids had a positive effect on reducing decisional conflict and improving decisional self-efficacy among patients with type 2 diabetes. Meanwhile, this article also revealed that patient decision aids have beneficial short-term effects on improving knowledge, but there was no significant long-term effect.

CONCLUSION

Patient decision aids are capable of becoming support tools to improve shared decision making. Further implementation studies are required to transform patient decision aids tools into clinical practice.

Factors Influencing Motivation and Engagement in Mobile Health Among Patients With Sickle Cell Disease in Low-Prevalence, High-Income Countries: Qualitative Exploration of Patient Requirements

Background

Sickle cell disease (SCD) is a hematological genetic disease affecting over 25 million people worldwide. The main clinical manifestations of SCD, hemolytic anemia and vaso-occlusion, lead to chronic pain and organ damages. With recent advances in childhood care, high-income countries have seen SCD drift from a disease of early childhood mortality to a neglected chronic disease of adulthood. In particular, coordinated, preventive, and comprehensive care for adults with SCD is largely underresourced. Consequently, patients are left to self-manage. Mobile health (mHealth) apps for chronic disease self-management are now flooding app stores. However, evidence remains unclear about their effectiveness, and the literature indicates low user engagement and poor adoption rates. Finally, few apps have been developed for people with SCD and none encompasses their numerous and complex self-care management needs.

Objective

This study aimed to identify factors that may influence the long-term engagement and user adoption of mHealth among the particularly isolated community of adult patients with SCD living in low-prevalence, high-income countries.

Methods

Semistructured interviews were conducted. Interviews were audiotaped, transcribed verbatim, and analyzed using thematic analysis. Analysis was informed by the Braun and Clarke framework and mapped to the COM-B model (capability, opportunity, motivation, and behavior). Results were classified into high-level functional requirements (FRs) and nonfunctional requirements (NFRs) to guide the development of future mHealth interventions.

Results

Overall, 6 males and 4 females were interviewed (aged between 21 and 55 years). Thirty FRs and 31 NFRs were extracted from the analysis. Most participants (8/10) were concerned about increasing their physical capabilities being able to stop pain symptoms quickly. Regarding the psychological capability aspects, all interviewees desired to receive trustworthy feedback on their self-care management practices. About their physical opportunities, most (7/10) expressed a strong desire to receive alerts when they would reach their own physiological limitations (ie, during physical activity). Concerning social opportunity, most (9/10) reported wanting to learn about the self-care practices of other patients. Relating to motivational aspects, many interviewees (6/10) stressed their need to learn how to avoid the symptoms and live as normal a life as possible. Finally, NFRs included inconspicuousness and customizability of user experience, automatic data collection, data shareability, and data privacy.

Conclusions

Our findings suggest that motivation and engagement with mHealth technologies among the studied population could be increased by providing features that clearly benefit them. Self-management support and self-care decision aid are patients’ major demands. As the complexity of SCD self-management requires a high cognitive load, pervasive health technologies such as wearable sensors, implantable devices, or inconspicuous conversational user interfaces should be explored to ease it. Some of the required technologies already exist but must be integrated, bundled, adapted, or improved to meet the specific needs of people with SCD.

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.