Surgical Clinics of North America

No abstract text is available yet for this article.

No abstract text is available yet for this article.

The adoption of digital health services in surgical care delivery is changing the patient experience. The goal of patient-generated health data monitoring incorporated with patient-centered education and feedback is to optimally prepare patients for surgery and personalize postoperative care to improve outcomes that matter to both patients and surgeons. Challenges include the need for the adoption of new methods for implementation and evaluation and equitable application of surgical digital health interventions, with considerations for accessibility as well as the development of new diagnostics and decision support that include the needs and characteristics of all populations served...

Data privacy in the United States is protected by a patchwork of Federal and state laws. Federal laws protect data based on the type of entity collecting and retaining the information. Unlike the European Union, there is no comprehensive privacy statute. Some statutes, such as the Health Insurance Portability and Accountability have specific requirements others like the Federal Trade Commission Act, only protect against deceptive and unfair business practices. Because of this framework, the use of personal data in the United States requires navigating through a series of complicated Federal and state statutes that are continuously being updated and amended...

Big Data is transforming health care. Characteristics of Big Data require data management strategies to effectively use, analyze, and apply the data. Clinicians are not typically learned in the fundamentals of these strategies which may cause a divide between collected data and data used. This article introduces the fundamentals of Big Data management and encourages clinicians to work with their information technology partners to further understand these processes and to identify opportunities for collaboration...

Applications for artificial intelligence (AI) and machine learning in surgery include image interpretation, data summarization, automated narrative construction, trajectory and risk prediction, and operative navigation and robotics. The pace of development has been exponential, and some AI applications are working well. However, demonstrations of clinical utility, validity, and equity have lagged algorithm development and limited widespread adoption of AI into clinical practice. Outdated computing infrastructure and regulatory challenges which promote data silos are key barriers...

Machine learning, a subtype of artificial intelligence, is an emerging field of surgical research dedicated to predictive modeling. From its inception, machine learning has been of interest in medical and surgical research. Built on traditional research metrics for optimal success, avenues of research include diagnostics, prognosis, operative timing, and surgical education, in a variety of surgical subspecialties. Machine learning represents an exciting and developing future in the world of surgical research that will not only allow for more personalized and comprehensive medical care...

The evolution of the knowledge economy and technology industry have fundamentally changed the learning environments occupied by contemporary surgical trainees and created pressures that will force the surgical community to consider. Although some learning differences are intrinsic to the generations themselves, these differences are primarily a function of the environments in which surgeons of different generations trained. Acknowledgment of the principles of connectivism and thoughtful integration of artificial intelligence and computerized decision support tools must play a central role in charting the future course of surgical education...

A cognitive bias describes "shortcuts" subconsciously applied to new scenarios to simplify decision-making. Unintentional introduction of cognitive bias in surgery may result in surgical diagnostic error that leads to delayed surgical care, unnecessary procedures, intraoperative complications, and delayed recognition of postoperative complications. Data suggest that surgical error secondary to the introduction of cognitive bias results in significant harm. Thus, debiasing is a growing area of research which urges practitioners to deliberately slow decision-making to reduce the effects of cognitive bias...

The practice of evidence-based medicine is the result of a multitude of research and trials aimed toward improving health-care outcomes. An understanding of the associated data remains paramount toward optimizing patient outcomes. Medical statistics commonly revolve around frequentist concepts that are convoluted and nonintuitive for nonstatisticians. Within this article, we will discuss frequentist statistics, their limitations, as well as introduce Bayesian statistics as an alternative approach for data interpretation...

The electronic medical record has fundamentally altered the way surgeons participate and practice medicine. There is now a wealth of data, once hidden behind paper records, that is, now available to surgeons to provide superior care to their patients. This article reviews the history of the electronic medical record, discusses use cases of additional data resources, and highlights the pitfalls of this relatively new technology.

Surgical decision-making is a continuum of judgments that take place during the preoperative, intraoperative, and postoperative periods. The fundamental, and most challenging, step is determining whether a patient will benefit from an intervention given the dynamic interplay of diagnostic, temporal, environmental, patient-centric, and surgeon-centric factors. The myriad combinations of these considerations generate a wide spectrum of reasonable therapeutic approaches within the standards of care. Although surgeons may seek evidenced-based practices to support their decision-making, threats to the validity of evidence and appropriate application of evidence may influence implementation...

The emergence of Big Data has been facilitated by technological advancements in the processing, storage, and analysis of large quantities of data. Its strength is derived from its size, ease of access, and speed of analysis, and it has enabled surgeons to investigate areas of interest that traditional research models have historically been unable to address. In the future, Big Data will likely assist in the incorporation of more advanced technologies into surgical practice, including artificial intelligence and machine learning to realize the full potential of Big Data in Surgery...

No abstract text is available yet for this article.

No abstract text is available yet for this article.

Traditionally, surgical therapy for primary breast lesions in stage IV breast cancer has been reserved for palliation. Several retrospective studies have suggested a possible survival benefit with surgical resection of the primary tumor in patients with distant metastases. However, evidence from prospective, randomized controlled trials suggest that locoregional control provides no clear survival advantage for patients with stage IV breast cancer. Future areas of inquiry include identification of subsets of patients who may derive a survival benefit from locoregional control...

The potential value of de-escalation in breast cancer therapy cannot be overstated. From reducing complications and morbidity of surgical therapy to the avoidance of chemotherapy in certain populations, the benefits of eliminating low-value therapies are significant. Further, those interventions that have minimal to no benefit may also further low-risk care cascades resulting in additional treatments or interventions without associated value, with increased financial toxicity, and resulting excess health care expenditures...

Among women, breast cancer remains the second leading cause of cancer death in the United States. Mammography remains the only validated screening tool to reduce breast cancer mortality. The American Society of Breast Surgeons recommends that average-risk women undergo breast cancer screening every year starting at age 40. This article reviews the fundamentals of mammography screening, current age-based mammography screening recommendations, supplemental breast cancer screening recommendations in high-risk women, and novel imaging technologies...

The cancer genome plays an increasingly large role in the care of patients with breast cancer. Commercially available gene-expression profiling assays are now a part of staging and treatment guidelines, and their use continues to be examined in large-scale studies. With the advent of next-generation sequencing, the cancer genome can now be examined more quickly, less invasively, and in much greater detail. These technologies have led to a more nuanced understanding of molecular pathways, allowing providers to better match patients to clinical trials...

Genetic testing plays an important role in assessing breast cancer risk and often the risk of other types of cancers. Accurate risk assessment and stratification represents a critical element of identifying who is best served by increased surveillance and consideration of other prevention or treatment options while also limiting overtreatment and unnecessary testing. The indications for testing will likely continue to expand, and ideally, more women with a genetic predisposition to breast cancer will be identified before they are diagnosed with breast cancer and thus have the option to consider effective screening and prevention management strategies...