The digital transformation of surgery.

Rapid advances in digital technology and artificial intelligence in recent years have already begun to transform many industries, and are beginning to make headway into healthcare. There is tremendous potential for new digital technologies to improve the care of surgical patients. In this piece, we highlight work being done to advance surgical care using machine learning, computer vision, wearable devices, remote patient monitoring, and virtual and augmented reality. We describe ways these technologies can be used to improve the practice of surgery, and discuss opportunities and challenges to their widespread adoption and use in operating rooms and at the bedside.

Has increased telehealth access during COVID-19 led to over-utilization of primary care?

Telehealth use for primary care has skyrocketed since the onset of the COVID-19 pandemic. Enthusiasts have praised this new medium of delivery as a way to increase access to care while potentially reducing spending. Over two years into the pandemic, the question of whether telehealth will lead to an increase in primary care utilization and spending has been met with contradictory answers. Some evidence suggests that telehealth may be used as an addition to in-person visits. Others like Dixit et al. have found that telehealth can actually substitute for in-person care rather than contribute to overutilization. As telehealth continues to evolve, outcomes, utilization, and quality of care should be closely monitored.

Intelligent risk prediction in public health using wearable device data.

The importance of infection risk prediction as a key public health measure has only been underscored by the COVID-19 pandemic. In a recent study, researchers use machine learning to develop an algorithm that predicts the risk of COVID-19 infection, by combining biometric data from wearable devices like Fitbit, with electronic symptom surveys. In doing so, they aim to increase the efficiency of test allocation when tracking disease spread in resource-limited settings. But the implications of technology that applies data from wearables stretch far beyond infection monitoring into healthcare delivery and research. The adoption and implementation of this type of technology will depend on regulation, impact on patient outcomes, and cost savings.

Defining digital surgery for the future.

Innovations in robotics, virtual and augmented reality, and artificial intelligence are being rapidly adopted as tools of "digital surgery". Despite its quickly emerging role, digital surgery is not well understood. A recent study defines the term itself, and then specifies ethical issues specific to the field. These include privacy and public trust, consent, and litigation.

Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation.

Health digital twins are defined as virtual representations ("digital twin") of patients ("physical twin") that are generated from multimodal patient data, population data, and real-time updates on patient and environmental variables. With appropriate use, HDTs can model random perturbations on the digital twin to gain insight into the expected behavior of the physical twin-offering groundbreaking applications in precision medicine, clinical trials, and public health. Main considerations for translating HDT research into clinical practice include computational requirements, clinical implementation, as well as data governance, and product oversight.

Leveraging reimbursement strategies to guide value-based adoption and utilization of medical AI.

With the increasing number of FDA-approved artificial intelligence (AI) systems, the financing of these technologies has become a primary gatekeeper to mass clinical adoption. Reimbursement models adapted for current payment schemes, including per-use rates, are feasible for early AI products. Alternative and complementary models may offer future payment options for value-based AI. A successful reimbursement strategy will align interests across stakeholders to guide value-based and cost-effective improvements to care.

Watching Parkinson's disease with wrist-based sensors.

Parkinson's disease (PD) lacks sensitive, objective, and reliable measures for disease progression and response. This presents a challenge for clinical trials given the multifaceted and fluctuating nature of PD symptoms. Innovations in digital health and wearable sensors promise to more precisely measure aspects of patient function and well-being. Beyond research trials, digital biomarkers and clinical outcome assessments may someday support clinician-initiated or closed-loop treatment adjustments. A recent study from Verily Life Sciences presents results for a smartwatch-based motor exam intended to accelerate the development and evaluation of therapies for PD.

An improved fluorescent noncanonical amino acid for measuring conformational distributions using time-resolved transition metal ion FRET.

With the recent explosion in high-resolution protein structures, one of the next frontiers in biology is elucidating the mechanisms by which conformational rearrangements in proteins are regulated to meet the needs of cells under changing conditions. Rigorously measuring protein energetics and dynamics requires the development of new methods that can resolve structural heterogeneity and conformational distributions. We have previously developed steady-state transition metal ion fluorescence resonance energy transfer (tmFRET) approaches using a fluorescent noncanonical amino acid donor (Anap) and transition metal ion acceptor to probe conformational rearrangements in soluble and membrane proteins. Here, we show that the fluorescent noncanonical amino acid Acd has superior photophysical properties that extend its utility as a donor for tmFRET. Using maltose-binding protein (MBP) expressed in mammalian cells as a model system, we show that Acd is comparable to Anap in steady-state tmFRET experiments and that its long, single-exponential lifetime is better suited for probing conformational distributions using time-resolved FRET. These experiments reveal differences in heterogeneity in the apo and holo conformational states of MBP and produce accurate quantification of the distributions among apo and holo conformational states at subsaturating maltose concentrations. Our new approach using Acd for time-resolved tmFRET sets the stage for measuring the energetics of conformational rearrangements in soluble and membrane proteins in near-native conditions.

Chronic Breast Pain Prior to Breast Cancer Surgery Is Associated with Worse Acute Postoperative Pain Outcomes.

Acute postoperative pain is associated with adverse short and long-term outcomes among women undergoing surgery for breast cancer. Previous studies identified preexisting pain as a predictor of postoperative pain, but rarely accounted for pain location or chronicity. This study leveraged a multinational pain registry, PAIN OUT, to: (1) characterize patient subgroups based on preexisting chronic breast pain status and (2) determine the association of preexisting chronic pain with acute postoperative pain-related patient-reported outcomes and opioid consumption following breast cancer surgery. The primary outcome was a composite score comprising the mean of pain intensity and pain interference items from the International Pain Outcomes Questionnaire. The secondary outcome was opioid consumption in the recovery room and ward. Among 1889 patients, we characterized three subgroups: no preexisting chronic pain (n = 1600); chronic preexisting pain elsewhere (n = 128) and; chronic preexisting pain in the breast with/without pain elsewhere (n = 161). Controlling for covariates, women with preexisting chronic breast pain experienced more severe acute postoperative pain and pain interference (ß = 1.0, 95% CI = 0.7-1.3, p < 0.001), and required higher doses of opioids postoperatively (ß = 2.7, 95% CI = 0.6-4.8, p = 0.013). Preexisting chronic breast pain may be an important risk factor for poor pain-related postoperative outcomes. Targeted intervention of this subgroup may improve recovery.