Keywords

1.1 Introduction

“Care for a medical condition (or a patient population) usually involves multiple specialties and numerous interventions. Value for the patient is created by providers’ combined efforts over the full cycle of care. The benefits of any one intervention for ultimate outcomes will depend on the effectiveness of other interventions throughout the care cycle” [1]. This sentence, reported in a seminal paper published by Michael E Porter, highlights the need to avoid the vision of “focused factories” concentrated on narrow groups of interventions to promote integrated practice units that are accountable for the total care for a medical condition and its complications. The progressive transition from the so-called “silos” models to more integrated and patient-centered systems (i.e., more focused on patient journey) in clinical medicine should involve also the practice of diagnostics.

1.2 Diagnosis and Diagnostics

The word “diagnosis” comes from the Greek prefix dia (apart) and gignōskein (discern, know). Taken together, the meaning of the word is to “know thoroughly” or to “know apart” (distinguish from another). In contemporary Western medicine, physicians move toward the closure of diagnostic possibilities through testing and objective analysis and by means of a “rule-out” reasoning (to ensure the life threatening and treatable conditions are quickly identified), which shall ultimately bring physicians to the correct clinical answer [2]. Diagnostic information may originate from any clinical interaction, examination, or test, including the patient’s history, signs and symptoms, laboratory and imaging studies, biopsy and other procedures, and physiological and functional assessments. Diagnosis serves as description of a patient’s condition and for guiding treatment and prognosis. An increased understanding of the genomic, proteomic, metabolomic, and microbiomic underpinnings of human biology over time has generated greater knowledge of etiology and progression of biological function from a healthy condition to a diseased state. As understanding of the precursors of disease grow more detailed and revealing, the art and science of diagnosis enlarge from detection of present disease to prediction of future illness. “Put in equivalent, positive terms, medical diagnosis moves from characterizing the current state of health to predicting the future state of health. Then interventions may be designed to enhance, maintain, and as needed, restore health” [3]. Despite these impressive improvements in understanding the pathophysiology and molecular nature of most acute and chronic diseases, multiple inefficiencies built into the clinical diagnostic testing landscape work against the seamless integration of clinical diagnostic testing into the treatment pathway. These inefficiencies result in slowing the adoption of laboratory and other diagnostic tests, deficient physician education, and delays in the inclusion of diagnostic testing in associated clinical guidelines—ultimately inhibiting the seamless delivery of these critical diagnostic tests [4]. In particular, the business model involved in delivery of laboratory and other diagnostic services seems to be “primarily designed and executed in individual silos driven by internal activities and managed according to performance metrics that match the discipline itself rather than the products of services to improve clinical pathways, clinical and economical outcomes and patient safety” [5, 6]. As such, clinical laboratories are increasingly organized as focused factories, with the goal of maximizing productivity, improving internal efficiency (e.g., by reducing the cost per test), and consolidating structures in mega-laboratories or even outsourcing testing to independent facilities. Several initiatives propose a rigorous team-building transformational organizational change, with a radical departure from the current hierarchical, silo-oriented, medical practice model focused on physician-centered tools, models, concepts, and the language to implement transformational patient-centered medical care [7].

1.3 Integrated Diagnostics

According to the World Health Organization (WHO), health services should be “managed and delivered so that people receive a continuum of health promotion, disease prevention, diagnosis, treatment, disease-management, rehabilitation and palliative care services, coordinated across the different levels and sites of care within and beyond the health sector, and according to their needs throughout the life course” [8]. “Integrated diagnostics” has been defined as “convergence of imaging, pathology, and laboratory tests with advanced information technology (IT)” [9]. In their paper, the authors emphasized that “diagnoses depend on multiple components that include not only imaging, but also clinical observation, pathology, laboratory, and genomic tests. To date, there is too little coordination between the medical specialties responsible for ordering and performing these tests, nor is there enough consideration as to the optimal order of tests. This will change in a world of integrated diagnostics, where, instead of relying on individual provider bias in the selection of tests, data from diverse sources will be used to determine the most efficient diagnostic algorithms. Imaging will be incorporated judiciously into these integrated diagnostic algorithms, complementing other diagnostic techniques in order to maximise efficiency and minimise waste.” Other authors emphasized the evidence that “Under the current paradigm of diagnostic medicine, pathologists and radiologists function as members of distinct disciplines, with no direct linkage between their workflows or reporting systems. Even when both departments belong to the same institution, their respective reports on the same patient are only loosely associated with one another by identifiers such as patient’s name and medical record number. Despite this complete bifurcation of reporting, the synthesis of both specialties’ data must establish diagnosis, determine prognosis, drive patient management and serve as the primary means for assessing response to treatment” [10]. Therefore, the better comprehension of several biological pathways, coupled with emerging technological advances, has recently fostered a paradigm shift in the way diagnostics have been for a long time acknowledged, paving the way to a new model of healthcare based on integration of different data coming from multiple and often independent sources. This, in turn, may allow a more rapid, efficient, and accurate clinical decision-making process, thus ultimately assuring better clinical and economical outcomes. Irrespective of clinical and environmental scenarios, several lines of evidence now attest that the role of the so-called “integrated diagnostics” will overwhelmingly emerge in the foreseeable future, allowing not only to make earlier and more accurate diagnoses, but also to save a large amount of human and economic resources [11]. In their article, the authors have reported several examples of the fundamental value of integrated diagnostics in most of the leading causes of morbidity and mortality in Western Countries such as acute myocardial infarction, stroke, venous thromboembolism, cancer, and infectious diseases. More recently, the coronavirus disease 2019 (COVID-19) pandemic has reinforced the need for more and better integration between all subdisciplines of laboratory medicine, as well as between pathology, genomic, and radiology [12]. The possible convergence of laboratory, pathology, and imaging test results within the same medical report is, therefore, a valuable goal to foster earlier and more accurate diagnoses, and personalized medicine. However, the enormous volumes of different information (the so-called “big-data”) should challenge the mind of clinicians and healthcare professionals. Reinforcement of clinical decision support through expert systems and algorithms based on machine learning and artificial intelligence will become unavoidable [13, 14], and is a major point for the education and training of the new generation of diagnostic professionals. The combination of big data and artificial intelligence, referred by some as the fourth industrial revolution, will change laboratory, radiology, and pathology along with other medical specialties. As predicted by some authors, because pathology and radiology have a similar past and a common destiny, these specialties should perhaps be merged into a single entity, the “information specialist,” whose responsibility will not be exclusively to extract information from laboratory data, images, and histology, but to manage the information extracted by artificial intelligence in the clinical context of the patient [15]. The integration of laboratory and pathology services with the creation of the acronym “PALM” (Pathology and Laboratory Medicine) has been recommended by Michael Wilson and Colleagues as a fundamental tool for assuring “accurate diagnosis and detection of disease, informing prognosis and guiding treatment, contributing to disease screening, public health surveillance and disease registries, and supporting medical-legal systems” [16]. A further challenge is represented by the need to integrate, particularly as concerns laboratory tests, the data from decentralized testing (point-of-care, near-patient, and home testing) and wearables [17], as shown in Fig. 1.1.

Fig. 1.1
A schematic diagram illustrates the integration of laboratory tests, data from decentralized testing, and wearables. The labels include laboratory medicine, pathology, radiology, P O T C slash wearable devices, palm, and integrated diagnostics.

Integrated diagnostics: convergence of laboratory medicine, pathology, radiology, and decentralized testing

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1.4 Conclusions

The quest for diagnostic excellence currently encounters many obstacles, including shortcomings in healthcare delivery that limit an efficient integration between several sources of information. The generation of a vast amount of data from the clinical laboratory, pathology genomics, and radiology does not automatically convert to meaningful conclusions and higher effectiveness in both diagnosis and patient treatment. Diagnostic integration and generation of unified medical reports, coupled with machine learning techniques, especially suited to analyze large amounts of data in real time, should be now adopted to foster an optimal diagnostic process and more specific, accurate, and complete diagnostic assessment. However, a combination of machine learning and human judgement should be taken for granted [18].