Multi-criteria decision-making (MCDM) methods are commonly used in many fields of research, e.g., engineering and manufacturing systems, water resources studies , medicine, and etc. However, there is ...no effective approach of selecting a MCDM method to problem, which is solved. The formal requirements of each MCDM method are not sufficient because most methods would seem to be appropriate for most problems. Therefore, the main purpose of the paper is a comparison of accuracy selected MCDM methods. Proposed approach is presented on the example of mortality in patients with acute coronary syndrome. Additionally, the paper presents characteristic objects method (COMET) as a potential decision making method for use in medical problems, which accuracy is compared with TOPSIS and AHP. In the experimental study, the average and standard deviation of the root mean square error of evaluations are examined for groups of randomly selected patients, each described by age, blood pressure, and heart rate. Then, the correctness of choosing the patient in the best and worst condition is also examined among randomly selected pairs. As a result of the experimental study, rankings obtained by the COMET method are distinctly more accurate than those obtained by TOPSIS or AHP techniques. The COMET method, in the opposite of others method, is completely free of the rank reversal phenomenon, which is identified as a main source of problems with evaluations accuracy.
Background
Personalised care planning is a collaborative process used in chronic condition management in which patients and clinicians identify and discuss problems caused by or related to the ...patient's condition, and develop a plan for tackling these. In essence it is a conversation, or series of conversations, in which they jointly agree goals and actions for managing the patient's condition.
Objectives
To assess the effects of personalised care planning for adults with long‐term health conditions compared to usual care (i.e. forms of care in which active involvement of patients in treatment and management decisions is not explicitly attempted or achieved).
Search methods
We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, ProQuest, clinicaltrials.gov and WHO International Clinical Trials Registry Platform to July 2013.
Selection criteria
We included randomised controlled trials and cluster‐randomised trials involving adults with long‐term conditions where the intervention included collaborative (between individual patients and clinicians) goal setting and action planning. We excluded studies where there was little or no opportunity for the patient to have meaningful influence on goal selection, choice of treatment or support package, or both.
Data collection and analysis
Two of three review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. The primary outcomes were effects on physical health, psychological health, subjective health status, and capabilities for self management. Secondary outcomes included effects on health‐related behaviours, resource use and costs, and type of intervention. A patient advisory group of people with experience of living with long‐term conditions advised on various aspects of the review, including the protocol, selection of outcome measures and emerging findings.
Main results
We included 19 studies involving a total of 10,856 participants. Twelve of these studies focused on diabetes, three on mental health, one on heart failure, one on end‐stage renal disease, one on asthma, and one on various chronic conditions. All 19 studies included components that were intended to support behaviour change among patients, involving either face‐to‐face or telephone support. All but three of the personalised care planning interventions took place in primary care or community settings; the remaining three were located in hospital clinics. There was some concern about risk of bias for each of the included studies in respect of one or more criteria, usually due to inadequate or unclear descriptions of research methods.
Physical health
Nine studies measured glycated haemoglobin (HbA1c), giving a combined mean difference (MD) between intervention and control of ‐0.24% (95% confidence interval (CI) ‐0.35 to ‐0.14), a small positive effect in favour of personalised care planning compared to usual care (moderate quality evidence).
Six studies measured systolic blood pressure, a combined mean difference of ‐2.64 mm/Hg (95% CI ‐4.47 to ‐0.82) favouring personalised care (moderate quality evidence). The pooled results from four studies showed no significant effect on diastolic blood pressure, MD ‐0.71 mm/Hg (95% CI ‐2.26 to 0.84).
We found no evidence of an effect on cholesterol (LDL‐C), standardised mean difference (SMD) 0.01 (95% CI ‐0.09 to 0.11) (five studies) or body mass index, MD ‐0.11 (95% CI ‐0.35 to 0.13) (four studies).
A single study of people with asthma reported that personalised care planning led to improvements in lung function and asthma control.
Psychological health
Six studies measured depression. We were able to pool results from five of these, giving an SMD of ‐0.36 (95% CI ‐0.52 to ‐0.20), a small effect in favour of personalised care (moderate quality evidence). The remaining study found greater improvement in the control group than the intervention group.
Four other studies used a variety of psychological measures that were conceptually different so could not be pooled. Of these, three found greater improvement for the personalised care group than the usual care group and one was too small to detect differences in outcomes.
Subjective health status
Ten studies used various patient‐reported measures of health status (or health‐related quality of life), including both generic health status measures and condition‐specific ones. We were able to pool data from three studies that used the SF‐36 or SF‐12, but found no effect on the physical component summary score SMD 0.16 (95% CI ‐0.05 to 0.38) or the mental component summary score SMD 0.07 (95% CI ‐0.15 to 0.28) (moderate quality evidence). Of the three other studies that measured generic health status, two found improvements related to personalised care and one did not.
Four studies measured condition‐specific health status. The combined results showed no difference between the intervention and control groups, SMD ‐0.01 (95% CI ‐0.11 to 0.10) (moderate quality evidence).
Self‐management capabilities
Nine studies looked at the effect of personalised care on self‐management capabilities using a variety of outcome measures, but they focused primarily on self efficacy. We were able to pool results from five studies that measured self efficacy, giving a small positive result in favour of personalised care planning: SMD 0.25 (95% CI 0.07 to 0.43) (moderate quality evidence).
A further five studies measured other attributes that contribute to self‐management capabilities. The results from these were mixed: two studies found evidence of an effect on patient activation, one found an effect on empowerment, and one found improvements in perceived interpersonal support.
Other outcomes
Pooled data from five studies on exercise levels showed no effect due to personalised care planning, but there was a positive effect on people's self‐reported ability to carry out self‐care activities: SMD 0.35 (95% CI 0.17 to 0.52).
We found no evidence of adverse effects due to personalised care planning.
The effects of personalised care planning were greater when more stages of the care planning cycle were completed, when contacts between patients and health professionals were more frequent, and when the patient's usual clinician was involved in the process.
Authors' conclusions
Personalised care planning leads to improvements in certain indicators of physical and psychological health status, and people's capability to self‐manage their condition when compared to usual care. The effects are not large, but they appear greater when the intervention is more comprehensive, more intensive, and better integrated into routine care.
This paper develops a parsimonious process-level theory that connects organizational structure to exploration and exploitation. Toward this end, it develops a mathematical model of organizational ...decision making that combines an information processing approach in the spirit of Sah and Stiglitz Sah RK, Stiglitz JE (1986) The architecture of economic systems: Hierarchies and polyarchies.
Amer. Econom. Rev.
76(4):716–727 with elements from signal detection theory. The model is first used to explore a “design space” of organizations and identify trade-offs and dominance relationships among alternative organization designs. The paper then studies open questions in the organization design literature, such as the extent to which exploration and exploitation can be produced by one organization and what is the effect of organization size on exploration. More broadly, this research speaks to calls for the introduction of more process-level explanations in the organizations literature. The paper concludes with testable hypotheses and managerially relevant insights.
Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for ...well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.
Objective
To identify determinants of shared decision making in patients with multiple myeloma (MM) to facilitate the design of a program to maximize the effects of shared decision making.
Methods
...This prospective longitudinal study recruited 276 adult patients (52% male, mean age 62.86 y, SD 15.45). Each patient completed the eHealth Literacy Scale (eHEALS), Multidimensional Trust in Health Care Systems Scale (MTHCSS), Patient Communication Pattern Scale (PCPS), and 9‐Item Shared Decision‐Making Questionnaire (SDM‐Q‐9) at baseline and the SDM‐Q‐9 again 6 months later. One family member of the patient completed the Family Decision‐Making Self‐Efficacy (FDMSE) at baseline. Structural equation modeling (SEM) was used to investigate the associations between eHealth literacy (eHEALS), trust in the health care system (MTHCSS), self‐efficacy in family decision making (FDMSE), patient communication pattern (PCPS), and shared decision making (SDM‐Q‐9).
Results
SEM showed satisfactory fit (comparative fit index = 0.988) and significant correlations between the following: eHealth literacy and trust in the health care system (β = 0.723, P < 0.001); eHealth literacy and patient communication pattern (β = 0.242, P < 0.001); trust in the health care system and patient communication pattern (β = 0.397, P < 0.001); self‐efficacy in family decision making and patient communication pattern (β = 0.264, P < 0.001); eHealth literacy and shared decision making (β = 0.267, P < 0.001); and patient communication pattern and shared decision making (β = 0.349, P < 0.001).
Conclusions
Patient communication and eHealth literacy were found to be important determinants of shared decision making. These factors should be taken into consideration when developing strategies to enhance the level of shared decision making.
Preterm birth is a global health priority. Using a progestogen during high-risk pregnancy could reduce preterm birth and adverse neonatal outcomes.
We did a systematic review of randomised trials ...comparing vaginal progesterone, intramuscular 17-hydroxyprogesterone caproate (17-OHPC), or oral progesterone with control, or with each other, in asymptomatic women at risk of preterm birth. We identified published and unpublished trials that completed primary data collection before July 30, 2016, (12 months before data collection began), by searching MEDLINE, Embase, CINAHL, the Maternity and Infant Care Database, and relevant trial registers between inception and July 30, 2019. Trials of progestogen to prevent early miscarriage or immediately-threatened preterm birth were excluded. Individual participant data were requested from investigators of eligible trials. Outcomes included preterm birth, early preterm birth, and mid-trimester birth. Adverse neonatal sequelae associated with early births were assessed using a composite of serious neonatal complications, and individually. Adverse maternal outcomes were investigated as a composite and individually. Individual participant data were checked and risk of bias assessed independently by two researchers. Primary meta-analyses used one-stage generalised linear mixed models that incorporated random effects to allow for heterogeneity across trials. This meta-analysis is registered with PROSPERO, CRD42017068299.
Initial searches identified 47 eligible trials. Individual participant data were available for 30 of these trials. An additional trial was later included in a targeted update. Data were therefore available from a total of 31 trials (11 644 women and 16185 offspring). Trials in singleton pregnancies included mostly women with previous spontaneous preterm birth or short cervix. Preterm birth before 34 weeks was reduced in such women who received vaginal progesterone (nine trials, 3769 women; relative risk RR 0·78, 95% CI 0·68–0·90), 17-OHPC (five trials, 3053 women; 0·83, 0·68–1·01), and oral progesterone (two trials, 183 women; 0·60, 0·41–0·90). Results for other birth and neonatal outcomes were consistently favourable, but less certain. A possible increase in maternal complications was suggested, but this was uncertain. We identified no consistent evidence of treatment interaction with any participant characteristics examined, although analyses within subpopulations questioned efficacy in women who did not have a short cervix. Trials in multifetal pregnancies mostly included women without additional risk factors. For twins, vaginal progesterone did not reduce preterm birth before 34 weeks (eight trials, 2046 women: RR 1·01, 95% CI 0·84–1·20) nor did 17-OHPC for twins or triplets (eight trials, 2253 women: 1·04, 0·92–1·18). Preterm premature rupture of membranes was increased with 17-OHPC exposure in multifetal gestations (rupture <34 weeks RR 1·59, 95% CI 1·15–2·22), but we found no consistent evidence of benefit or harm for other outcomes with either vaginal progesterone or 17-OHPC.
Vaginal progesterone and 17-OHPC both reduced birth before 34 weeks' gestation in high-risk singleton pregnancies. Given increased underlying risk, absolute risk reduction is greater for women with a short cervix, hence treatment might be most useful for these women. Evidence for oral progesterone is insufficient to support its use. Shared decision making with woman with high-risk singleton pregnancies should discuss an individual's risk, potential benefits, harms and practicalities of intervention. Treatment of unselected multifetal pregnancies with a progestogen is not supported by the evidence.
Patient-Centered Outcomes Research Institute.
All adaptive organisms face the fundamental tradeoff between pursuing a known reward (exploitation) and sampling lesser-known options in search of something better (exploration). Theory suggests at ...least two strategies for solving this dilemma: a directed strategy in which choices are explicitly biased toward information seeking, and a random strategy in which decision noise leads to exploration by chance. In this work we investigated the extent to which humans use these two strategies. In our "Horizon task," participants made explore-exploit decisions in two contexts that differed in the number of choices that they would make in the future (the time horizon). Participants were allowed to make either a single choice in each game (horizon 1), or 6 sequential choices (horizon 6), giving them more opportunity to explore. By modeling the behavior in these two conditions, we were able to measure exploration-related changes in decision making and quantify the contributions of the two strategies to behavior. We found that participants were more information seeking and had higher decision noise with the longer horizon, suggesting that humans use both strategies to solve the exploration-exploitation dilemma. We thus conclude that both information seeking and choice variability can be controlled and put to use in the service of exploration.
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