[Comorbidity in medical guidelines: comparison of the current state, epidemiologic models and expert opinion]

Sensitivity for multimorbidity: The role of diagnostic uncertainty of physicians when evaluating multimorbid video case-based vignettes

Background

Multimorbidity can be defined as the co-occurrence of two or more chronic medical conditions in one person. Within the diagnostic process, accurately detecting a multimorbid disease pattern still poses a major challenge for most physicians, and is known as a source of diagnostic uncertainty.

Objective

We investigated, how sensitive, confident, and accurate physicians are in diagnosing multimorbid versus monomorbid patients.

Methods

We created eight video case-based vignettes, which differed in type of morbidity (multimorbid versus monomorbid), field of medical specialization (somatic versus mental), and relatedness of underlying diseases (causally related versus unrelated). In total, 74 physicians (GPs, residents in an emergency department and psychiatrists) watched three to five randomly allocated video cases and had to generate suspected diagnoses at the end of each of three video sequences. Additionally, participating physicians rated subjective confidence for all mentioned diagnoses and for three sequences per case with the help of confidence profiles.

Results

Altogether, physicians made a large number of accurate diagnoses (69%). Nevertheless, the overall number of underdiagnosed multimorbid cases (misses) was significantly higher (71%) than over-diagnosed monomorbid cases (false alarms) (7%).

Discussion

According to Signal Detection Theory, GPs and psychiatrists both showed lower detection performance for medical cases that lay beyond their own field of specialization. Remarkably, residents show the highest sensitivity for multimorbid cases with an approximately identically detection performance d' slightly over 1 for both field of medical specialization (somatic and mental). Furthermore, higher uncertainty in diagnosing multimorbid cases is related to lower confidence especially at the beginning of a diagnostic process, as well as to unrelated and therefore probably rare disease pattern. Several limitations of the study and the video case-based vignettes are described within the discussion section.

Conclusions

Physicians have to be sensitized for multimorbidity even more, and have to be taught in the prevalence of existing disease combinations. Communicating uncertainty with other specialists could be helpful when faced with a sometimes “fuzzy” pattern of symptoms.

Development of a meta-algorithm for guiding primary care encounters for patients with multimorbidity using evidence-based and case-based guideline development methodology

OBJECTIVE

The study aimed to develop a comprehensive algorithm (meta-algorithm) for primary care encounters of patients with multimorbidity. We used a novel, case-based and evidence-based procedure to overcome methodological difficulties in guideline development for patients with complex care needs.

STUDY DESIGN

Systematic guideline development methodology including systematic evidence retrieval (guideline synopses), expert opinions and informal and formal consensus procedures.

SETTING

Primary care.

INTERVENTION

The meta-algorithm was developed in six steps:1. Designing 10 case vignettes of patients with multimorbidity (common, epidemiologically confirmed disease patterns and/or particularly challenging health care needs) in a multidisciplinary workshop.2. Based on the main diagnoses, a systematic guideline synopsis of evidence-based and consensus-based clinical practice guidelines was prepared. The recommendations were prioritised according to the clinical and psychosocial characteristics of the case vignettes.3. Case vignettes along with the respective guideline recommendations were validated and specifically commented on by an external panel of practicing general practitioners (GPs).4. Guideline recommendations and experts' opinions were summarised as case specific management recommendations (N-of-one guidelines).5. Healthcare preferences of patients with multimorbidity were elicited from a systematic literature review and supplemented with information from qualitative interviews.6. All N-of-one guidelines were analysed using pattern recognition to identify common decision nodes and care elements. These elements were put together to form a generic meta-algorithm.

RESULTS

The resulting meta-algorithm reflects the logic of a GP's encounter of a patient with multimorbidity regarding decision-making situations, communication needs and priorities. It can be filled with the complex problems of individual patients and hereby offer guidance to the practitioner. Contrary to simple, symptom-oriented algorithms, the meta-algorithm illustrates a superordinate process that permanently keeps the entire patient in view.

CONCLUSION

The meta-algorithm represents the back bone of the multimorbidity guideline of the German College of General Practitioners and Family Physicians. This article presents solely the development phase; the meta-algorithm needs to be piloted before it can be implemented.