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Record W2944581065 · doi:10.21693/1933-088x-9.4.200

Shared Decision Making: Promoting Best Practice

2010· article· en· W2944581065 on OpenAlexaboutno aff
Arlene G. Schiro

Bibliographic record

VenueAdvances in Pulmonary Hypertension · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCommissionPublic relationsPresentation (obstetrics)Ideal (ethics)PsychologyMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

With the welcomed increase in treatment options that we now have in the field of pulmonary hypertension (PH), it is important that our commitment to patients includes going beyond patient education and a presentation of benefits and risks to make sure we reach consensus on the choice of treatment. As health professionals we are increasingly encouraged to involve patients in their treatment decisions, recognizing them as experts with unique knowledge of their own preferences based on their values and willingness to comply.1 In fact, noncompliance is less likely if both parties decide together which treatment is best and move forward with it. However, finding ways to elicit patients' preferences can be a considerable challenge. Physician bias or time constraints may influence how and to what degree these preferences are elicited, and many physicians wish to retain the imbalance of power between themselves and their patients, thus causing patients to be reluctant to share their preferences if they regard their doctor as more powerful.2 Now that we have multiple treatment options, what are the best ways to promote shared decision making?Since the president's commission first coined the term “shared decision making” over 30 years ago, the concept has become an ethical ideal.3 This concept goes beyond simply informing the patient of the risks and benefits of therapy options and then making recommendations, but rather goes further by assisting the patient in becoming dynamically involved in the decision process and, ultimately, the outcome.4 One common example of such a treatment decision would be whether the patient should initiate intravenous or subcutaneous prostacyclin for functional class III pulmonary arterial hypertension (PAH). How do we facilitate a final decision that would satisfy both the clinicians and patient? What would happen if the patient prefers to start with the nebulized form of prostacyclin despite progressive symptoms rather than an intravenous formulation recommended by the physician and/or team? These can be daunting decisions encountered repeatedly in practice. In such situations, the stresses of the patient's clinical status as well as information overload may influence the ability of that patient to take an active role in choosing the best long-term therapy option. Compliance may also become an issue if he/she did not take an active role in the treatment decision. Another scenario would be a conflict between what the patient wants and what a family member expects. A clue to this issue, termed “decisional dilemma,” may be when the patient asks caregivers, “what would you do?” This dilemma can be even more concerning while obtaining informed consent for participation in a clinical drug trial.How a physician makes a decision and how a patient and his/her family decide on a treatment can be accomplished through different approaches.5 Clinicians rely on utility theory or statistics to analyze best outcomes. The best example of this is the current evidence-based treatment algorithms derived from 15 years of clinical trials.6 These are also based on a consensus within the PH medical community and identified as core principles within our practice. On the other hand, patients and family members are more likely to use the information-processing theory, focusing on cognitive and affective variables integrating their values and preferences. One way in which we as team members can bridge this gap of utility theory vs information processing is by using a patient decision aid. Decision aids or tools help the patient become an informed participant and assist in reaching decisions in line with the patient's values, preferences, and life goals while taking into consideration the information provided by the physician.7 Over the last decade empirical research examining the importance of “shared decision making” has fueled increased interest in shifting it from ethical ideal to actual practice by a growing movement of developing and standardizing decision aids.8The Ottawa Personal Decision Guide (OPDG), devised by O'Connor, Stacey, and Jacobson at the Ottawa Hospital Research Institute, is useful in assisting the decision-making process for patients with PH who are deciding on options for lifelong therapies. The OPDG (Figure 1) helps the patient delineate options and asks him/her to place personal weights on benefits and risks. In addition, the OPDG further assists the patient in clarifying personal values and support systems. This tool should be used as a complement, rather than a replacement, for counseling and discussions with the medical team. It can be an excellent option for patients who need assistance identifying the best treatment option. Since it is interactive and available in 4 languages, it is easy to use and applicable to a varied group. The patient can use this tool to focus on knowledge (needed facts and possible questions), values (what matters most), and supports (financial, opinions, pressures). Once completed, it can then be printed and brought to the clinic as a catalyst for an open discussion with the PH team.Information alone, however, is not always sufficient for patients in making such an important decision. Providing information to the patient should only be a prerequisite for assistance in making the best choice for lifelong treatment. All parties must contribute to the treatment decision for the process to be shared. The use of a decisional aid such as the OPDG is just one way in which we can facilitate information and preference sharing leading to a focused discussion. The imaginative use of available decision aids together with our evidence-based treatment algorithms and risk assessments generated from clinical trials and registries keep us in the mainstream of health care. We must realize that shared decision making is not an illusion but rather another way that we can work collaboratively with our patients to achieve the best outcomes. As members of the PH Resource Network, we must work together with our physician colleagues to find timely and cost-effective ways to meet this challenge.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.308
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2010
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