Clinical Interviewing: An Essential but Neglected Method of Medicine.

Clinical interviewing is the basic method to understand how a person feels and what are the presenting complaints, obtain medical history, evaluate personal attitudes and behavior related to health and disease, give the patient information about diagnosis, prognosis, and treatment, and establish a bond between patient and physician that is crucial for shared decision making and self-management. However, the value of this basic skill is threatened by time pressures and emphasis on technology. Current health care trends privilege expensive tests and procedures and tag the time devoted to interaction with the patient as lacking cost-effectiveness. Instead, the time spent to inquire about problems and life setting may actually help to avoid further testing, procedures, and referrals. Moreover, the dialogue between patient and physician is an essential instrument to increase patient's motivation to engage in healthy behavior. The aim of this paper was to provide an overview of clinical interviewing and its optimal use in relation to style, flow and hypothesis testing, clinical domains, modifications according to settings and goals, and teaching. This review points to the primacy of interviewing in the clinical process. The quality of interviewing determines the quality of data that are collected and, eventually, of assessment and treatment. Thus, interviewing deserves more attention in educational training and more space in clinical encounters than it is currently receiving.

Development of a Novel Clinical Risk Score for COVID-19 Infections.

OBJECTIVE: The ongoing emergence of novel severe acute respiratory syndrome coronavirus 2 strains such as the Omicron variant amplifies the need for precision in predicting severe COVID-19 outcomes. This study presents a machine learning model, tailored to the evolving COVID-19 landscape, emphasizing novel risk factors and refining the definition of severe outcomes to predict the risk of a patient experiencing severe disease more accurately. METHODS: Utilizing electronic health records from the Healthjump database, this retrospective study examined over 1 million US COVID-19 diagnoses from March 2020 to September 2022. Our model predicts severe outcomes, including acute respiratory failure, intensive care unit admission, or ventilator use, circumventing biases associated with hospitalization, which exhibited ∼4× geographical variance of the new outcome. RESULTS: The model exceeded similar predictors with an area under the curve of 0.83 without lab data to predict patient risk. It identifies new risk factors, including acute care history, health care encounters, and distinct medication use. An increase in severe outcomes, typically 2-3× higher than subsequent months, was observed at the onset of each new strain era, followed by a plateau phase, but the risk factors remain consistent across strain eras. CONCLUSION: We offer an improved machine learning model and risk score for predicting severe outcomes during changing COVID-19 strain eras. By emphasizing a more clinically precise definition of severe outcomes, the study provides insights for resource allocation and intervention strategies, aiming to better patient outcomes and reduce health care strain. The necessity for regular model updates is highlighted to maintain relevance amidst the rapidly evolving COVID-19 epidemic.

Long COVID and Medicine's Two Cultures.

Medicine has separated the two cultures of biological science and social science in research, even though they are intimately connected in the lives of our patients. To understand the cause, progression, and treatment of long COVID , biology and biography, the patient's lived experience, must be studied together.

Population health science as a unifying foundation for translational clinical and public health research.

Separated both in academics and practice since the Rockefeller Foundation effort to "liberate" public health from perceived subservience to clinical medicine a century ago, research in public health and clinical medicine have evolved separately. Today, translational research in population health science offers a means of fostering their convergence, with potentially great benefit to both domains. Although evidence that the two fields need not and should not be entirely distinct in their methods and goals has been accumulating for over a decade, the prodigious efforts of biomedical and social sciences over the past year to address the COVID-19 pandemic has placed this unifying approach to translational research in both fields in a new light. Specifically, the coalescence of clinical and population-level strategies to control disease and novel uses of population-level data and tools in research relating to the pandemic have illuminated a promising future for translational research. We exploit this unique window to re-examine how translational research is conducted and where it may be going. We first discuss the transformation that has transpired in the research firmament over the past two decades and the opportunities these changes afford. Next, we present some of the challenges-technical, cultural, legal, and ethical- that need attention if these opportunities are to be successfully exploited. Finally, we present some recommendations for addressing these challenges.

Rethinking Table 1.

Clinical and translational medicine studies of disease risk or treatment response typically include a table 1 comparing groups on age, sex, and race and/or ethnicity. Although customarily treated as biological variables, each denote biography, elements of a person's lived experience. Capturing these biographical features is essential to achieving the ambition of personalized medicine.

Biosocial medicine: Biology, biography, and the tailored care of the patient.

Biosocial Medicine, with its emphasis on the full integration of the person's biology and biography, proposes a strategy for clinical research and the practice of medicine that is transformative for the care of individual patients. In this paper, we argue that Biology is one component of what makes a person unique, but it does not do so alone. Biography, the lived experience of the person, integrates with biology to create a unique signature for each individual and is the foundational concept on which Biosocial Medicine is based. Biosocial Medicine starts with the premise that the individual patient is the focus of clinical care, and that average results for "ideal" patients in population level research cannot substitute for the "real" patient for whom clinical decisions are needed. The paper begins with a description of the case-based method of clinical reasoning, considers the strengths and limitations of Randomized Controlled Trials and Evidence Based Medicine, reviews the increasing focus on precision medicine and then explores the neglected role of biography as part of a new approach to the tailored care of patients. After a review of the analytical challenges in Biosocial Medicine, the paper concludes by linking the physician's commitment to understanding the patient's biography as a critical element in developing trust with the patient.

Clinimetric Criteria for Patient-Reported Outcome Measures.

Patient-reported outcome measures (PROMs) are self-rated scales and indices developed to improve the detection of the patients' subjective experience. Given that a considerable number of PROMs are available, it is important to evaluate their validity and usefulness in a specific research or clinical setting. Published guidelines, based on psychometric criteria, do not fit in with the complexity of clinical challenges, because of their quest for homogeneity of components and inadequate attention to sensitivity. Psychometric theory has stifled the field and led to the routine use of scales widely accepted yet with a history of poor performance. Clinimetrics, the science of clinical measurements, may provide a more suitable conceptual and methodological framework. The aims of this paper are to outline the major limitations of the psychometric model and to provide criteria for clinimetric patient-reported outcome measures (CLIPROMs). The characteristics related to reliability, sensitivity, validity, and clinical utility of instruments are critically reviewed, with particular reference to the differences between clinimetric and psychometric approaches. Of note is the fact that PROMs, rating scales, and indices developed according to psychometric criteria may display relevant clinimetric properties. The present paper underpins the importance of the clini-metric methodology in choosing the appropriate PROMs. CLIPROM criteria may also guide the development of new indices and the validation of existing PROMs to be employed in clinical settings.

The Biological and Biographical Basis of Precision Medicine.

Construction of a patient narrative (case history) is a core strategy in the care of patients. Recent advances in biomarker identification and digital sensors to monitor physiological and behavioral features have made constructing a case history more complex. Notably, however, although a biological profile is increasingly a part of the patient's profile, an analogous patient-based biographical (life experience) profile is typically overlooked. Evolving concepts such as allostasis and allostatic load refer to processes promoting stability of physiological systems in the presence of diverse life experiences. Integrating details of both biology and biography is a goal of "precision medicine." In this review, we describe how complex interactions between biology and biography affect disease risk and treatment response and highlight a strategy to develop narratives that establish the integration of biology and biography as the scientific basis for precision medicine.

Biology, biography, and the translational gap.

Medicine-based evidence integrates a patient's biology and biography to improve individual medical care and to help eliminate the translational gap between research and the clinic.

The Use of 5-Alpha Reductase Inhibitors to Manage Benign Prostatic Hyperplasia and the Risk of All-cause Mortality.

OBJECTIVE: To compare the risk of mortality among men treated for benign prostatic hyperplasia (BPH) with 5 alpha-reductase inhibitors (5ARI) to those treated with alpha-blockers (AB) in community practice settings. METHODS: We employed a retrospective matched cohort study in 4 regions of an integrated healthcare system. Men aged 50 years and older who initiated pharmaceutical treatment for BPH and/or lower urinary tract symptoms between 1992 and 2008 and had at least 3 consecutive prescriptions that were eligible and followed through 2010 (N = 174,895). Adjusted hazard ratios were used to estimate the risk of mortality due to all-causes associated with 5ARI use (with or without concomitant ABs) as compared to AB use. RESULTS: In this large and diverse sample with 543,523 person-years of follow-up, 35,266 men died during the study period, 18.9% of the 5ARI users and 20.4% of the AB users. After adjustment for age, medication initiation year, race, region, prior AB history, Charlson score, and comorbidities, 5ARI use was not associated with an increased risk of mortality when compared to AB use (Adjusted hazard ratios: 0.64, 95% confidence interval: 0.62, 0.66). CONCLUSION: Among men receiving medications for BPH in community practice settings, 5ARI use was not associated with an increased risk of mortality when compared to AB use. These data provide reassurance about the safety of using 5ARIs in general practice to manage BPH and/or lower urinary tract symptoms.

Medicine based evidence and personalized care of patients.

BACKGROUND: For the past 70- years patient care has been dominated by Evidence Based Medicine (EBM) with its emphasis on Randomized Controlled Trials (RCTs) and clinical guidelines to standardize medical decision-making. METHODS: Critical assessment of the literature and analyses of the arguments that favor patient care based primarily on individual variability in disease risk or treatment response versus emphasis on group standardization. RESULTS: Medicine Based Evidence (MBE) is used to guide decision making for an individual patient at hand by profiling the clinical features (biology) and life experience (biography) of the patient and then finding approximate matches to the patient in a clinical library of patients assembled from diverse sources (RCTs, cohorts, registries, electronic health records and more). CONCLUSION: Medicine is transitioning from population based model of clinical care that relies on average results from RCTs to an individual-based model of "personalized" medicine. For individualized care of the patient at hand, MBE is the preferred scientific strategy to generate evidence for patient care.