When Joe Biden served as Vice President under Barack Obama, he was instrumental in passing the Affordable Care Act, one of the largest healthcare overhauls in the history of the country. The confluence of his history with healthcare and the COVID-19 pandemic made healthcare changes a focal point of the 2020 election. Some of Biden’s major pledges included expanding access to healthcare, simplifying the system, and lowering prescription drug prices [1]. Given that Democrats now control all three branches of government, there is a strong chance that significant parts of Biden’s planned healthcare changes will be passed. 

Within his first few days in office, Biden instituted changes to the United States’ vaccine infrastructure. The White House announced on January 27 that vaccine distribution would be increased to provide 200 million doses by the end of summer. Around the same time, Biden also indicated that he would expand the country’s testing infrastructure and authorize FEMA to create federally run community vaccination centers [2]. While the effects of these rapid changes to the country’s vaccine infrastructure will play out over the next few years, they will undoubtably result in several permanent changes, such as higher domestic production of vaccines, more attention to supply chain stability, and increased reliance on telemedicine [3]. 

Besides COVID-19, the Biden administration is facing considerable pressure from Democrats to expand insurance coverage. In February, two Democratic congressmen introduced the Medicare-X Choice Act, which would establish a public option for small businesses and individuals by 2025. The bill was first proposed in 2017 and allows the Health and Human Services secretary to negotiate drug prices for Medicare plans. It also expands subsidies and tax credits to many low- and middle-income Americans [4]. An analysis of the previous version of the bill by the American Hospital Association found that it would reduce healthcare spending by $1.2 trillion in the first decade after it goes into action. That would result in additional stresses to hospitals and other care facilities, half of which would likely see negative margins after the law goes into effect [5]. 

Regardless of whether Medicare-X passes, the Biden administration will likely take steps to reduce the cost of prescription drugs, which became a hot-button issue during the 2020 election. In 2019, prescription drugs cost Americans a collective $370 billion, a price which is expected to rise by 5% annually between 2021 and 2028 [6]. As a result, the issue has wide bipartisan support. The Biden administration’s approach will likely include allowing Medicare to negotiate drug prices and limit yearly increases on drug prices. Biden has pledged to do so by building on the ACA, a proposal that is likely to meet fierce opposition, both from Republican opponents who seek to dismantle the legislation and progressive Democrats who seek to institute a true public option [7]. 

While the Biden administration has indicated that healthcare reform is one of its top issues, the COVID-19 pandemic has taken the front seat. Additionally, while some healthcare legislation has been introduced, the Biden administration has remained focused on passing the $1.9 trillion economic stimulus package and a comprehensive immigration bill. As a result, the future and timeline of healthcare changes remains uncertain. 

References 

[1] Silberner, Joanne. “How Joe Biden Plans to Heal American Healthcare.” BMJ, 19 Jan. 2021, doi:10.1136/bmj.n142. 

[2] Morse, Susan. “Biden Ramps up Vaccine Distribution to 200 Million Doses by the End of Summer.” Healthcare Finance News, HIMSS Media, 27 Jan. 2021, www.healthcarefinancenews.com/news/biden-ramps-vaccine-distribution-200-million-doses-end-summer.   

[3] Galea, Sandro, et al. “Taking the Long View: COVID-19 Priorities for the Biden Administration.” Journal of Health Politics, Policy and Law, 2021, doi:10.1215/03616878-8970781.  

[4] Pramuk, Jacob. “Senate Democrats Push for Public Option as Biden Weighs Health-Care Reform Plans.” CNBC, CNBC, 19 Feb. 2021, www.cnbc.com/2021/02/19/democrats-kaine-and-bennet-introduce-health-care-public-option-bill.html.   

[5] Koenig, Lane. “The Impact of Medicare-X Choice on Coverage, Healthcare Use, and Hospitals: AHA.” American Hospital Association, 12 Mar. 2019, www.aha.org/position-paper/2019-04-30-impact-medicare-x-choice-coverage-healthcare-use-and-hospitals.  

[6] Keehan, Sean P., et al. “National Health Expenditure Projections, 2019–28: Expected Rebound In Prices Drives Rising Spending Growth.” Health Affairs, vol. 39, no. 4, 2020, pp. 704–714., doi:10.1377/hlthaff.2020.00094.  

[7] Gavulic, Kyle A., and Stacie B. Dusetzina. “Prescription Drug Priorities under the Biden Administration.” Journal of Health Politics, Policy and Law, 22 Jan. 2021, doi:10.1215/03616878-8970810.  

Apps and websites such as WebMD, Ada, Babylon Health, and FamilyDoctor aim to provide diagnoses and triage advice to patients, given self-reported symptom(s), demographics, and other information. Such tools have the potential to enable health screening and let patients take charge of their own health. Patients would have more information on when to administer self-care and avoid unnecessary health bills, while providers could devote more time to patients who genuinely need care. However, obstacles such as mediocre accuracy, low usability, and low awareness among potential users might hamper the adoption of these tools in the short term.  

If apps for symptom analysis and advice are less accurate than recommendations from general practitioners (GPs), patients could become misinformed about their own health or fail to pursue necessary care. One can measure accuracy in several ways: accuracy of diagnosis, accuracy of triage advice, and the breadth of symptom coverage. In a study of eight symptom analysis apps, one app had accuracy on par with GPs across all three metrics, while two other apps had on-par accuracy with respect to two metrics [1]. In another study, 23 symptom analysis apps were found to recommend emergency or primary care in cases where self-care would be appropriate [2]. Although these apps might be generally inaccurate today, their developers will continue to make improvements, and the apps will gain accuracy over time.  

Research has shown that patients also care about factors beyond accuracy. A study of young adult users of symptom analysis apps emphasized the importance of personalization, security, and privacy. Participants perceived symptom analysis apps to be more useful for triage advice than diagnosis [3]. In a separate study, participants tended to perceive Google as a more familiar and flexible tool than symptom analysis apps. After using several symptom analysis tools, they realized that Google was not as usable as they thought [4]. These findings suggest that patients would respond well to apps with strong outreach campaigns and transparency about their underlying processes.  

To summarize the aforementioned findings, symptom analysis apps for physical health are well–studied, with some being almost as effective as a general practitioner. The question remains whether symptom analysis apps for mental health are equally effective. A review of sixteen such apps found that clinical research backed fourteen of those apps, but none of the apps covered more than one or two mental disorders. Furthermore, roughly half of the apps were targeted to a specific population, like veterans or university students. This lack of breadth is a far cry from the breadth of coverage displayed in most apps for physical symptom analysis. To overcome this challenge, developers will need to release mental health apps for wider populations and types of disorders. At the same time, the proliferation of mental health apps in recent years has led researchers to urge caution. One study claims that “the majority” of mental health apps have no evidence of efficacy, calling for greater clinician involvement before the disparity between apps and practitioners widens too much [6]. As with physical health, inaccurate mental health advice might mislead or harm patients.  

By bringing medical expertise to mobile devices, apps for symptom analysis are already widening access to medical advice. With the proper technical and legal protections, trustworthy stakeholders could leverage search data from symptom analysis apps to monitor public health. From March to April 2020, such data from nearly 100,000 participants in Germany and the United Kingdom proved effective in flagging the COVID-19 pandemic as it unfolded [7]. As these apps become more popular, data-driven predictions in public health may become more accurate and more mainstream.  

References 

[1] Gilbert S., et al. How Accurate Are Digital Symptom Assessment Apps for Suggesting Conditions and Urgency Advice? A Clinical Vignettes Comparison to GPs. The BMJ 2020. DOI:10.1136/bmjopen-2020-040269.  

[2] Semigran H. L., et al. Evaluation of Symptom Checkers for Self Diagnosis and Triage: Audit Study. The BMJ 2015. DOI:10.1136/bmj.h3480.  

[3] Aboueid S., et al. Young Adults’ Perspectives on the Use of Symptom Checkers for Self-Triage and Self-Diagnosis: Qualitative Study. JMIR Public Health and Surveillance 2021; 7: 1. DOI:10.2196/22637.  

[4] Li H., and Salah H. Comparing Four Online Symptom Checking Tools: Preliminary Results. Proceedings of the 2017 Industrial and Systems Engineering Conference.  

[5] Wang K., et al. A Systematic Review of the Effectiveness of Mobile Apps for Monitoring and Management of Mental Health Symptoms or Disorders. Journal of Psychiatric Research 2018; 107. DOI:j.jpsychires.2018.10.006.  

[6] Marshall J. M., et al. Clinical or Gimmickal: The Use and Effectiveness of Mobile Mental Health Apps for Treating Anxiety and Depression. Australian & New Zealand Journal of Psychiatry 2020; 54: 1. DOI:10.1177/0004867419876700.  

[7] Mehl A., et al. Syndromic Surveillance Insights from a Symptom Assessment App Before and During COVID-19 Measures in Germany and the United Kingdom: Results From Repeated Cross-Sectional Analyses. JMIR mHealth and uHealth 2020; 8: 10. DOI:2020/10/e21364.  

Continuous infiltration of a surgical wound after an operation is commonly used for analgesic purposes. Specific methods of wound infiltration include patient-controlled analgesia and continuous infusion [1]. Research to determine the effectiveness of wound infiltration analgesia has examined a variety of metrics, in particular VAS pain scores and opioid use. This research has sought to determine not only how wound infusion compares to other analgesic techniques, but which drug types, drug doses, and surgical situations are optimal for this technique. 

Most literature on this topic indicates that infiltration decreases postoperative pain as measured by VAS scores. In one trial, patients undergoing total knee arthroplasty were divided into two groups, one of which received wound infiltration for analgesia. The other received an epidural infusion. The former group had lower resting VAS scores in the 24 hours following surgery—7 compared to the epidural infusion group’s 30. The trend remained even between 48 and 96 hours, where respective VAS scores were 7.5 and 23 [2]. Similarly, in a randomized trial, patients recovering from total hip arthroplasty received either wound infiltration or epidural infusion for analgesia. For 20 hours following surgery, the groups’ VAS scores showed little difference. However, from 20-96 hours, the infiltration group had significantly lower at-rest VAS scores: for instance, 8 rather than 20 between 24 and 48 hours [3]. 

However, in another study, thyroid surgery patients in the infiltration group showed no significant difference in pain scores compared to the placebo group [4]. Wound infiltration also did not correlate with a significant reduction in opioid use. The total dose administered for the infiltration group was 64 mg, compared to 69 mg for the infusion group [4] . However, other research points to a significant reduction in opioid use for infiltration patients. In the above knee arthroplasty study, for instance, patients in the infiltration group were administered a mean of 7.5 mg of morphine in the 24 hours following surgery, while their counterparts received 18 mg on average [2]. In the previously mentioned study of hip arthroplasty patients, meanwhile, the infiltration group consumed a mean of 258 mg in the 96 hours after surgery, while the infusion group consumed a mean of 324 mg [3]. Finally, in a review of 203 articles examining the efficacy of the technique, researchers found that “a general reduction in pain intensity and in opioid consumption has been observed with continuous wound infiltration” [1]. 

In this review, Paladini et al. also found that wound infiltration has varying degrees of effectiveness for different procedures. Specifically, infiltration appeared most effective in areas with large amounts of connective and subcutaneous tissue. In addition, the effectiveness of infiltration varies depending on the type and amount of anesthetic being administered [1]. In one study, patients undergoing shoulder surgery were divided into three groups. Two received a continuous infiltration of saline, while the others received infiltration of ropivacaine at different concentrations—2 mg/mL and 3.75 mg/mL respectively. While both ropivacaine groups enjoyed lower VAS scores and consumed fewer opioids than the saline group, the group receiving the higher concentration of the drug had significantly lower VAS scores and opioid consumption than the low-concentration group.  

Thus, wound infiltration appears generally effective for reducing post-operative pain across a variety of metrics. However, the method may be more successful in the context of certain operations rather than others, and its success may also depend upon the dosage and type of anesthetic being used. 

References 

[1] Paladini, Giuseppe, et al. “Continuous Wound Infiltration of Local Anesthetics in Postoperative Pain Management: Safety, Efficacy and Current Perspectives.” Journal of Pain Research, vol. 13, 2020, pp. 285-294., doi:10.2147/JPR.S211234.

[2] Andersen, Karen V, et al. “A Randomized, Controlled Trial Comparing Local Infiltration Analgesia with Epidural Infusion for Total Knee Arthroplasty.” Acta Orthopaedica, vol. 81, no. 5, 2010, pp. 606–610., doi:10.3109/17453674.2010.519165. 

[3] Andersen, Karen V, et al. “Reduced Hospital Stay and Narcotic Consumption, and Improved Mobilization with Local and Intraarticular Infiltration after Hip Arthroplasty: A Randomized Clinical Trial of an Intraarticular Technique versus Epidural Infusion in 80 Patients.” Acta Orthopaedica, vol. 78, no. 2, 2007, pp. 180–186., doi:10.1080/17453670710013654.  

[4] Miu, Mihaela, et al. “Lack of Analgesic Effect Induced by Ropivacaine Wound Infiltration in Thyroid Surgery.” Anesthesia & Analgesia, vol. 122, no. 2, 2016, pp. 559–564., doi:10.1213/ane.0000000000001041. 

[5] Gottschalk, Andre, et al. “Continuous Wound Infiltration with Ropivacaine Reduces Pain and Analgesic Requirement After Shoulder Surgery.” Anesthesia & Analgesia, 2003, pp. 1086–1091., doi:10.1213/01.ane.0000081733.77457.79. 

Since the outbreak of SARS-CoV-2, the body of scientific literature on COVID-19 has rapidly expanded [1]. Current research often focuses on COVID-19 antibodies, which provide valuable information on the continuing spread of the virus, previous infection patterns, and the immune response [1]. 

Widespread availability of commercial assays that detect SARS-CoV-2 antibodies has enabled researchers to examine acquired immunity to COVID-19 at the population level [3]. The four major types of antibody tests are rapid diagnostic tests (RDT), enzyme-linked immunosorbent assays (ELISA), neutralization assays, and chemiluminescent immunoassays [4]. Currently, there is no standard antibody test for detecting SARS-CoV-2 antibodies [5]. Antibody tests for SARS-CoV-2 sense the presence of IgA, IgM, or IgG antibodies produced by B cells [4]. IgM antibodies are produced soon after infection, while IgG antibodies are produced later to maintain the immune response to a specific pathogen [4]. IgA is found on mucous membranes and assists the innate immune response [4]. New clinical reports indicate that antibodies against SARS-CoV-2 form between 6 and 10 days after infection, with peak IgM antibody levels at 12 days [4]. These IgM antibodies persist for up to 35 days [4]. In contrast, IgG antibodies peak at 17 days and persist for up to 49 days [4]. 

Higher antibody titers have been discovered in men than women, despite women generally having more B cells and producing more antibodies than men [7]. Observed during the acute stage of SARS-CoV-2 infection, higher antibody titers in men correlate with men showing more severe symptoms and experiencing a higher fatality rate [8]. Conversely, women have shown increased resistance against SARS-CoV-2 [8]. This may be due to the enhanced nature of innate antiviral responses, such as those mediated by toll-like receptors, in women [8]. 

The relationship between the presence of SARS-CoV-2 antibodies and the risk of subsequent COVID-19 reinfection remains unclear [6]. Data from a recent study completed at the Oxford University Hospitals in the United Kingdom suggests that the presence of SARS-CoV-2 IgG antibodies is associated with a substantially reduced risk of reinfection for 6 months [6]. The researchers performed a prospective longitudinal cohort study of 12,541 health care workers to assess the relative incidence of positive COVID-19 tests in those who were seropositive for SARS-CoV-2 antibodies and in those who were seronegative [6]. Of the 11,364 health care workers who followed up after an initial negative antibody result, 223 received a positive COVID-19 test [6]. Of the 1,265 health care workers who followed up after an initial positive antibody result, only 2 received a positive COVID-19 test [6]. It may be possible that SARS-CoV-2 protective immunity lasts longer than 6 months [9]. In November 2020, there had been more than 30 million confirmed infections, but few documented cases of reinfection with SARS-CoV-2 throughout the world [9]. 

An analysis of 20,000 patients with COVID-19 in the United States concluded that convalescent plasma therapy with neutralizing antibodies is safe and may reduce mortality in critically ill patients [10]. Neutralizing antibodies can be passively transferred into patients before or after viral infection to prevent or treat disease [10]. Therapeutic neutralizing antibodies with high specificity and strong affinity to target proteins have been used to treat several viral infections, including Ebola virus and influenza virus [10]. The neutralizing antibodies against SARS-CoV-2 that have been investigated so far all target spike proteins on the surface of the coronavirus [10]. Plasma containing neutralizing antibodies from convalescent individuals infected with SARS-CoV-2 currently is being administered to severely ill patients [10]. Current research finds that transfusion of such plasma to critically ill patients has resulted in reduced or undetectable viral loads and relieved acute respiratory distress syndrome [10]. 

References 

1. Figueiredo‐Campos, P., Blankenhaus, B., Mota, C.,. et al. (2020). Seroprevalence of anti‐SARS‐CoV‐2 antibodies in COVID‐19 patients and healthy volunteers up to 6 months post disease onset. European Journal of Immunology, 50(12), 2025-2040. doi:10.1002/eji.202048970 
2. Altmann, D., Douek, D., & Boyton, R. (2020). What policy makers need to know about COVID-19 protective immunity. The Lancet, 395(10236), 1527-1529. doi:10.1016/s0140-6736(20)30985-5 
3. Spellberg, B., Nielsen, T., & Casadevall, A. (2020). Antibodies, Immunity, and COVID-19. JAMA Internal Medicine. doi:10.1001/jamainternmed.2020.7986 
4. Kopel, J., Goyal, H., & Perisetti, A. (2020). Antibody tests for COVID-19. Baylor University Medical Center Proceedings, 34(1), 63-72. doi:10.1080/08998280.2020.1829261 
5. Weinstein, M., Freedberg, K., Hyle, E., & Paltiel, A. (2020). Waiting for Certainty on Covid-19 Antibody Tests—At What Cost?. New England Journal of Medicine. doi:10.1056/NEJMp2017739 
6. Lumley, S., O’Donnell, D., Stoesser, N., et al. (2020). Antibody Status and Incidence of SARS-CoV-2 Infection in Health Care Workers. New England Journal of Medicine. doi:10.1056/nejmoa2034545 
7. Robbiani, D., Gaebler, C., Muecksch, F.,et al. (2020). Convergent antibody responses to SARS-CoV-2 infection in convalescent individuals. bioRxiv. doi:10.1101/2020.05.13.092619 
8. Jin, J. M., Bai, P., He, W., et al. (2020). Gender differences in patients with COVID-19: Focus on severity and mortality. Frontiers in Public Health, 8, 152. doi:10.3389/fpubh.2020.00152 
9. Tillett, R., Sevinsky, J., Hartley, P., et al. (2020). Genomic evidence for reinfection with SARS-CoV-2: a case study. The Lancet Infectious Diseases. doi:10.1016/S1473-3099(20)30764-7 
10. Jiang, S., Zhang, X., Yang, Y., Hotez, P., & Du, L. (2020). Neutralizing antibodies for the treatment of COVID-19. Nature Biomedical Engineering, 4(12), 1134-1139. doi:10.1038/s41551-020-00660-2 

Despite efforts to improve perioperative patient safety over the past two decades, medical errors remain a significant cause of morbidity and mortality [1]. Safety improvement research has long been hindered by the weak relationship between healthcare interventions and adverse outcomes like morbidity and mortality [2]. These outcomes are rare in clinical research, limiting statistical power to demonstrate associations [2]. As a result, traditional methods of measuring morbidity and mortality have indicated inconsistent relationships with healthcare interventions [2]. Recently, an alternative to rare outcome measures, known as the “nonroutine event,” has been proposed [2]. 

A nonroutine event is defined as any aspect of clinical care perceived by clinicians or observers as a deviation from optimal care for a patient in a specific clinical scenario [3]. The concept of a nonroutine event includes not only the occurrence or near-occurrence of patient injury but also flawed care processes such as missing or broken equipment, delayed lab tests, insufficient training, and interpersonal communication errors [4,5]. It encompasses incidents that may not be directly linked to patient injury, which previously have been unreliably documented by reporting systems [4]. The nonroutine event reporting system is modeled after safety processes in the nuclear power industry where any deviation from optimal operating procedures is reported and investigated [4]. The foundation of this safety concept is represented by the principle of the “accident triangle” that relates frequent, low-importance events to infrequent, high-importance events such as morbidity and mortality [4]. 

The nonroutine event concept is broader than previous measurements used to assess clinical performance and medical error [2]. Because most nonroutine events do not involve errors by the care provider and few lead to patient injury, nonroutine events allow researchers to study underlying system processes without the negative implications of medical error [2,5].  

Initially, nonroutine events were used to retrospectively analyze workflow disruptions in anesthesia teams [5]. A 2002 study completed by researchers affiliated with the University of California, San Diego investigated the prevalence of nonroutine events in anesthesia care [6]. Anesthesiologists spend roughly 45% of the initial set-up time at the start of a normal workday on drug and fluid tasks, such as obtaining and filling syringes [6]. In observing anesthesiologists complete 68 drug– and fluid–related tasks, the researchers noted several nonroutine events including: difficulty finding anesthesia supplies, providers bumping into or tripping over IV poles or lines, malfunctioning infusion pumps, and blood leaking from IVs [6]. The results of the study suggested that many anesthesia drug and fluid tasks are inefficient, which may promote medical error [6]. 

The observed high incidence of nonroutine events has made it possible to collect prospective data to improve safety systems [6]. Today, nonroutine events are also used to assess the workflow of various surgical teams and team performance in the operating room [5]. A 2019 study completed at Children’s National Hospital in Washington, D.C. investigated the incidence of nonroutine events during pediatric trauma resuscitation [1]. The researchers reviewed 39 resuscitations and identified 337 nonroutine events [1]. The most frequent nonroutine event was failure to stabilize the cervical spine [1]. The results of the study highlighted common errors during pediatric trauma resuscitation that may lead to adverse outcomes [1]. 

Medical errors compromising patient safety and resulting in patient harm remain a significant health burden [1]. The nonroutine event concept provides a system to collect detailed information about types of deviations from optimal care and to show associations with long-term patient outcomes [4]. 

References 

1. Webman, R., Fritzeen, J., Yang, J., Ye, G., Mullan, P., & Qureshi, F. et al. (2016). Classification and team response to nonroutine events occurring during pediatric trauma resuscitation. Journal of Trauma and Acute Care Surgery, 81(4), 666-673. doi:10.1097/ta.0000000000001196 

1. Lane-Fall, M., & Bass, E. (2020). “Nonroutine Events” as a Nonroutine Outcome for Perioperative Systems Research. Anesthesiology, 133(1), 8-10. doi:10.1097/aln.0000000000003125 

1. Wacker, J. (2010). Managing Non-Routine Events in Anesthesia–A Concept to Measure and Improve Anesthesia Quality. Human Factors, 52(2):282-294. doi:10.1177/0018720809359178 

1. Liberman, J., Slagle, J., Whitney, G., Shotwell, M., Lorinc, A., Porterfield, E., & Weinger, M. (2020). Incidence and Classification of Nonroutine Events during Anesthesia Care. Anesthesiology, 133(1), 41-52. doi:10.1097/aln.0000000000003336 

1. Law, K. E., Hildebrand, E. A., Hawthorne, H. J., Hallbeck, M. S., Branaghan, R. J., Dowdy, S. C., & Blocker, R. C. (2019). A pilot study of non-routine events in gynecological surgery: Type, impact, and effect. Gynecologic Oncology, 152(2), 298-303. doi:10.1016/j.ygyno.2018.11.035 

1. Weinger, M. (2002). Human Factors Research in Anesthesia Patient Safety: Techniques to Elucidate Factors Affecting Clinical Task Performance and Decision Making. Journal of The American Medical Informatics Association, 9(90061), 58S-63. doi:10.1197/jamia.m1229