Clinical decision‐making: predictors of patient participation in nursing care

Aim. To investigate predictors of patients’ preferences for participation in clinical decision‐making in inpatient nursing care. Background. Patient participation in decision‐making in nursing care is regarded as a prerequisite for good clinical practice regarding the person’s autonomy and integrity. Design. A cross‐sectional survey of 428 persons, newly discharged from inpatient care. Methods. The survey was conducted using the Control Preference Scale. Multiple logistic regression analysis was used for testing the association of patient characteristics with preferences for participation. Results. Patients, in general, preferred adopting a passive role. However, predictors for adopting an active participatory role were the patient’s gender (odds ratio = 1·8), education (odds ratio = 2·2), living condition (odds ratio = 1·8) and occupational status (odds ratio = 2·0). A probability of 53% was estimated, which female senior citizens with at least a high school degree and who lived alone would prefer an active role in clinical decision‐making. At the same time, a working cohabiting male with less than a high school degree had a probability of 8% for active participation in clinical decision‐making in nursing care. Conclusions. Patient preferences for participation differed considerably and are best elicited by assessment of the individual patient. Relevance to clinical practice. The nurses have a professional responsibility to act in such a way that patients can participate and make decisions according to their own values from an informed position. Access to knowledge of patients’ basic assumptions and preferences for participation is of great value for nurses in the care process. There is a need for nurses to use structured methods and tools for eliciting individual patient preferences regarding participation in clinical decision‐making.     

The impact of electronic medical records on patient–doctor communication during consultation: a narrative literature review

Rationale, aims and objective  The effect of Electronic Medical Record (EMR) use on Patient–Doctor Communication (PDC) has rarely been studied. As data accumulate, the purpose of this article is to review the literature on EMR effect on PDC, to identify recurring themes and to offer preliminary guidelines and future directions for medical education and research.
Method  A database search was conducted and 14 articles that met inclusion criteria (published in the past 10 years, empirical investigations, direct assessment of the EMR impact on patient–doctor communication) were selected for review. A qualitative, grounded theory-like approach was employed to analyse the data.
Results  EMR use often has a positive impact on information exchange, but exerts a negative influence on patient centredness. Some physician characteristics such as their computer skills and behavioural style assist in overcoming this negative influence.
Conclusion  The use of EMR exerts both positive and negative impacts on physician–patient relationships. The negative impacts can be overcome by some simple means as well as better designs of EMR systems and medical education interventions. Physicians' everyday practices of integrating EMR use into the clinical encounter as well as better design of EMR systems and EMR and communication training may facilitate PDC in computerized settings.     

Clinical anisotropy: A case for shared decision making in the age of too much data and patient dis‐integration

Today, in the age of big data, we are more capable than ever before. But even having the world at our disposal with naught but the touch of a button, we find ourselves exceedingly vulnerable in the patient chair. With insurmountable amounts of knowledge being published and disseminated around the world, how can clinicians keep up and what can be done about it? And sitting in the patient chair, bewildered by the ever‐changing landscape of medicine at the blink of an eye, how can we, as patients, ever hope to be part of the conversations revolving around our own health? In this work, we explore the present‐day problems of big data in the clinical context, how failing to integrate patients can result in detrimental outcomes, and what shared decision making can do about it.     

Evaluation of a Nutrition Care Process‐based audit instrument,the Diet‐NCP‐Audit,for documentation of dietetic care in medical records

Adequate documentation in medical records is important for high‐quality health care. Documentation quality is widely studied within nursing, but studies are lacking within dietetic care. The aim of this study was to translate, elaborate and evaluate an audit instrument, based on the four‐step Nutrition Care Process model, for documentation of dietetic care in medical records. The audit instrument includes 14 items focused on essential parts of dietetic care and the documentation's clarity and structure. Each item is to be rated 0–1 or 0–2 points, with a maximum total instrument score of 26. A detailed manual was added to facilitate the interpretation and increase the reliability of the instrument. The instrument is based on a similar tool initiated 9 years ago in the United States, which in this study was translated to Swedish and further elaborated. The translated and further elaborated instrument was named Diet‐NCP‐Audit. Firstly, the content validity of the Diet‐NCP‐Audit instrument was tested by five experienced dietitians. They rated the relevance and clarity of the included items. After a first rating, minor improvements were made. After the second rating, the Content Validity Indexes were 1.0, and the Clarity Index was 0.98. Secondly, to test the reliability, four dietitians reviewed 20 systematically collected dietetic notes independently using the audit instrument. Before the review, a calibration process was performed. A comparison of the reviews was performed, which resulted in a moderate inter‐rater agreement with Krippendorff's α = 0.65–0.67. Grouping the audit results in three levels: lower, medium or higher range, a Krippendorff's α of 0.74 was considered high reliability. Also, an intra‐rater reliability test–retest with a 9 weeks interval, performed by one dietitian, showed strong agreement. To conclude, the evaluated audit instrument had high content validity and moderate to high reliability and can be used in auditing documentation of dietetic care.     

Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital

Objective  A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation. Methods  Embase, PubMed, and Web of Science databases were searched for clinical analytics derived from electronic health records in adult inpatient and emergency department settings between 2015 and 2021. Evidence was coded from the final study selection that related to (1) dashboard implementation challenges, (2) methods to overcome implementation challenges, and (3) dashboard assessment and impact. The evidences obtained, together with evidence extracted from relevant prior reviews, were mapped to an existing digital health transformation model to derive a conceptual framework for LHS analytics implementation. Results  A total of 238 candidate articles were reviewed and 14 met inclusion criteria. From the selected studies, we extracted 37 implementation challenges and 64 methods employed to overcome such challenges. We identified common approaches for evaluating the implementation of clinical dashboards. Six studies assessed clinical process outcomes and only four studies evaluated patient health outcomes. A conceptual framework for implementing the analytics of an LHS was developed. Conclusion  Health care organizations face diverse challenges when trying to implement real-time data analytics. These challenges have shifted over the past decade. While prior reviews identified fundamental information problems, such as data size and complexity, our review uncovered more postpilot challenges, such as supporting diverse users, workflows, and user-interface screens. Our review identified practical methods to overcome these challenges which have been incorporated into a conceptual framework. It is hoped this framework will support health care organizations deploying near-real-time clinical dashboards and progress toward an LHS.