Network Research Highlight: Selecting Fairly

By: Keaton Fletcher

A paper recently published in the International Journal of Selection and Assessment by a team of researchers including WSC Advisory Council Member, Deborah Rupp, focuses on an increasingly popular tool that organizations are using to select individuals for hiring or promotion, assessment centers. Assessment centers require participants to engage in a range of prescribed tasks designed to elicit the competencies that will be needed on the job. Trained raters then evaluate the individual across each of these competencies based on what was shown during the task. Despite being resource intensive, assessment centers are useful tools for organizations as they have been shown to predict future job performance, and historically have been viewed as a fairer and/or less biased method of evaluation than other potential tools organizations could use. However, some evidence (Dean, 2008) suggests that assessment centers may not be as free of bias as one might hope. There have been consistent findings to suggest a leniency effect (i.e., willingness to provide higher scores) towards white individuals and women. There has also been some evidence (Schmitt, 1993) to suggest assessment centers may be susceptible to a similar-to-me effect, in that the rater scores participants who are similar to the rater on demographic variables (e.g., race, gender) more favorably. Although combining ratings across multiple raters may help reduce the impact of these issues, Thornton, Rupp, Gibbons, and Vanhove (2019) argued that it is imperative to know if these biases are present even prior to aggregating across raters.

Using data from 189 police officers who were participating in an assessment center for a promotion, Thornton and colleagues (2019) explored how the leniency and similar-to-me effects might appear in the real world. For this particular assessment center, participants completed three different tasks for three separate pairs of judges. The judges were advanced police officers who had received training on the assessment center and were given materials design to minimize potential bias. The results of the study are generally encouraging, suggesting that bias was minimal when it was present. The authors did find some evidence for a leniency effect for white participants across all tasks, and inconsistent findings regarding leniency for women, or similar-to-me effects. Certainly this does not suggest that all assessment centers are free of bias, but should provide some hope that given the appropriate design, rater training and materials, an assessment center may have minimal bias. In minimizing the bias in assessment centers, organizations can increase the diversity in their internal pool of candidates at each level of the organization, creating the potential for succession planning that may facilitate organizational diversity from the executive level, down.

Demographics of Science and Engineering: Are We Improving?

By: Yendi Neil

According to a report released by the National Science Foundation (NSF; 2017), the enrollment of historically underrepresented groups (e.g., women, Blacks, Hispanics, Native Americans, Alaska Natives, and individuals with disabilities) in undergraduate institutions is increasing. However, enrollment trends differ across demographic groups. Hispanics, Native Hawaiians or Pacific Islanders, and American Indians or Alaska Natives are more likely to enroll in public 2-year colleges than other racial groups. Blacks and Native Hawaiians or Pacific Islanders are more likely to enroll in private, for-profit colleges, compared to other racial groups. Whites, Asians, and students who identify as multi-racial are more likely to enroll in private, nonprofit colleges (NSF, 2017). Additionally, people with disabilities have similar acceptance rates to the college of their choice compared to individuals without disabilities. Lastly, more women are enrolling in college than men. Taken together, these data suggest that the landscape of higher education is changing, and although certain types of institutions may still see a lower representation of minority racial groups, representation of women and individuals with disabilities has significantly improved.

The data suggest that over the past 20 years, women have increased in participation across all scientific fields of study. Women account for at least 70% of the graduates in psychology (at the baccalaureate, master’s, and doctoral levels), but only about 20% of degrees in physics. Engineering, computer sciences, economics, and mathematics/statistics also see a fairly low representation of women at all levels. Women of underrepresented minorities have higher rates of enrollment at all degree levels across science and engineering (S&E) degrees, than men of underrepresented minorities.

Roughly half of the S&E workforce is white men, white women and Asian men each account for about 15% of the S&E workforce, Asian women account for 7% of the workforce, and men of other racial groups account for 8%, while women of other racial groups account for 5%. Underrepresented racial minority scientists are more likely than others to be employed by the government, while women, regardless of race, are more likely than men to be employed by educational institutions.

Overall, the data in this report suggest that although the representation of women, minority racial groups, and individuals with disabilities has broadly increased across all levels within S&E, there are particular areas that might benefit from intervention to further increase enrollment and retention of these groups. Specifically, interventions could target more narrow domains of S&E (e.g., physics, computer science) that are struggling with the enrollment of women and underrepresented minorities. Interventions could also target transition points (e.g., from undergraduate to graduate programs, from school to the workforce), helping to ensure women, particularly women of underrepresented minority groups, transition successfully, at rates equivalent to other groups.

Network Research Highlight: Work-Family Conflict is a Barrier for Women

By: Elizabeth Moraff

Work Science Center network member Mary F. Fox has focused much of her research on women in research and academia, particularly noting barriers to their advancement. Most recently, she published a reflection on Georgia Tech’s website detailing the insights present research has provided on the way work-family conflict (when work interferes with family) and family-work conflict (when family interferes with work) operate differently among men and women in various stages of their academic careers. One of study’s most startling findings, Fox notes, shows women at the most senior levels of their academic career report more family-work conflict than those in earlier stages (Fox, 2019). She ends her reflection by indicating men have benefited more from gender-neutral employee policies in academia than women have. For instance, gender-neutral parental leave policy implementation correlates with fewer women achieving tenure, according to some studies (Fox, 2019).

Oddly, the prevalence of work-family and family-work conflict among professional women contrasts with perceptions of barriers that Fox found in earlier research. In a study of women engineers and their experiences with international research collaboration, she and her co-investigators predicted that women would cite external barriers as more influential in whether or not they had collaborated on research with international partners (Fox, Realff, Rueda, & Morn 2017). In particular, they posited female engineers would list difficulty acquiring funding and international research collaborators as the two greatest barriers in conducting collaborative research internationally. They were right! Women engineers did, in fact, list those two external barriers as most daunting. They rated these barriers as significantly more important than conflicts with balancing family and work (Fox et. al 2017).

Interestingly, these same female engineers imagined that balancing work and family would pose a significant obstacle to other women, but not to themselves (Fox et. al, 2017). Essentially, the engineers in the study identified external hurdles, funding and finding research collaborators, as the factors hindering collaborative international research in their own lives, but they envisioned other women as falling prey to more internal tensions (Fox et. al, 2017). In that study’s conclusion, Fox and her co-investigators point out that organizational policies are much more adept at addressing external barriers. Funding programs could close some of the perceived gap in acquiring resources for research. Initiatives connecting international women in similar fields could aid in the cross-pollination necessary for international work. Unfortunately, organizational policies are much more clumsy at alleviating the internal stressors, like work-family and family-work conflict, as Fox reveals in her reflection (2019).

Still, these two works from Dr. Fox suggest an additional dilemma: even women in competitive academic fields have adopted a narrative that says women are more likely to struggle with family obligations in their career, but they often fail to acknowledge those tensions in their own lives. Indeed, even though the research Fox pulls from paints a picture of work-family and family-work conflict straining women at the highest echelons of academia, women in equivalent roles in engineering do not name such stress as their main problem. Perhaps this perception reflects reality, perhaps not. Research indicates that professional women are rewarded for downplaying family obligations (Fox, 2019). It’s possible that female engineers are loath to recognize this stigmatized stressor in their lives despite recognition of its prevalence in the lives of other women. More research would have to answer such a question. In the end, Fox’s two works do show that women in advanced fields face a variety of barriers to their achievement, and that organizations must continue to implement strategic, evidence-based policies to tackle them.

Who Quits STEM Majors?

By Jacqueline Jung

The modern workforce is becoming increasingly science and technology based. Improving the selection and retention of undergraduate students in STEM (Science, Technology, Engineering, and Math) majors is, therefore, increasingly important. Attrition rates are high: more than 1 in 4 students leave college before completing their degree, and it is even more difficult to attract and retain students in STEM majors. This indicates both failure in selection and in identifying students who are at risk for attrition. To solve this problem, we must find or develop strong predictors of academic success at the post-secondary level to reduce the number of students who leave before getting a degree and increase the number of students who complete their degree, particularly students considering STEM majors and careers. Traditional predictors of academic performance include GPA and aptitude/intelligence tests, such as the SAT or ACT. More recently, efforts have been made to include measures of personality, motivational traits and skills, vocational interests, and other psychological measures as predictors of academic success. Although it may be controversial to use these measures for admissions given that they are self-reported, they could be used post-matriculation for identifying those students who are at-risk for attrition either from a STEM major or from the university as a whole.

A 2013 study (Ackerman, Kanfer, & Beier) followed 589 college students from their admission to the school to their graduation or attrition. First, the authors defined five combinations of traits, motivations, and self-perceptions that were common among the students: Math/Science Self-Concept (e.g., you prefer and feel confident in your abilities regarding math and science), Mastery/Organization (e.g., you want to learn, you are organized and conscientious), Openness and Verbal Self-Concept (e.g., you feel confident in your verbal abilities and you are open to new experiences and critical thinking), Anxiety in Achievement Contexts (e.g., you are neurotic and have high levels of test anxiety), and Extroversion/Sociability (e.g., you want status and an easy life, you prefer and feel confident in social contexts). Using these five combinations of traits (i.e., trait complexes), academic records, high school GPA, SAT scores, and AP exam scores, the authors predicted student (1) academic success (measured by GPA), (2) STEM major persistence (STEM-persisters were those whose initial and final major was STEM, whereas STEM-leavers were those whose initial major was STEM, but final was not), and (3) attrition (students who did not complete a degree within eight years of matriculation) better than traditional measures alone would.

Interestingly, women scored lower than men in the Math/Science Self-Concept trait-complex, but scored higher than men in Mastery/Organization, Anxiety in Achievement, and Extroversion/Sociability trait-complexes. An equal proportion (17.6%) of men and women who started college in a STEM major left STEM, yet the men who left the STEM major had high Math/Science Self-Concepts whereas the women had low Math/Science Self-Concepts, similar to those women who were never in a STEM major. Alternatively, the men who left STEM majors were low in Mastery/Organization, lower than even those men who were never in a STEM major. This may suggest that men and women quit STEM for different reasons: women who leave do not feel confident in their STEM abilities and men who leave lack the organizational skills necessary to succeed.

Certainly, more research is needed to really understand why students may leave their chosen educational paths, but this study alone provides some insight into what might be happening. Schools or organizations interested in increasing the pool of talented candidates in STEM majors may work on interventions that increase STEM self-concept, particularly for women, and improve organizational skills and self-regulation, particularly for men.

Network Research Highlight: Work is More Than a Paycheck

By: Keaton Fletcher

WSC Advisory Board Member, David Blustein, is part of a team led by Kelsey L. Autin that recently published a paper in the Journal of Counseling Psychology that tackles what it means to have your needs satisfied by your work. The authors point toward decent work as a method that allows individuals to meet their needs. Although there has been historically been great debate about what constitutes human needs, the authors assert that work can meet your survival needs, social connection needs, and self-determination needs (autonomy, relatedness, and competence). By putting food on the table, a roof over your head, providing you with a sense that you are doing something meaningful, giving you an opportunity to take control of certain aspects of your life, make deep meaningful relationships, and feel like you have successfully mastered something, work contributes greatly to the human experience.

In a survey of 476 people working at least part time within the U.S., the authors found that if your job meets your survival needs you also tend to be more satisfied in your life, but not necessarily the job yourself. If the job meets your social connection needs, that you also tend to be more satisfied in both your life and your job. If the job meets your self-determination needs, you similarly are more satisfied in your job and our life, but the relationship between self-determination needs and job satisfaction is the strongest relationship found in the study. That said, whether your job meets your survival needs was the best predictor of life satisfaction.

For researchers, this study focuses primarily on the development and validation of a scale designed to actually measure whether a job is meeting an individual’s needs.

Lack of Sleep is a Public Health and Economic Concern

By: Riley Swab

Sleep is necessary to increase focus and productivity, both vital traits to workers. Japanese workers, however, are accumulating massive sleep debts (i.e., consistently sleeping less than 7 hours per night without rebound sleep such as naps or sleeping in) due to a prevalent mentality that sacrificing sleep means you are working hard (Lewis, 2018). The issue has become so prevalent that in 2014, Japan’s Ministry of Health, Labor, and Welfare acknowledged the existence of a “sleep is expendable” attitude in many Japanese workers (Lewis, 2018). The report, in fact, claimed that 71 percent of Japanese adult male workers slept less than seven hours a night (Lewis, 2018). Although Japanese workers’ lack of sleep has reached an extreme state, a lack of sleep in workers is not unique to Japan. The CDC collected sleep data in 2014 and found that 40% of American adults were getting less than seven hours of sleep (the minimum recommended by the CDC for the best health and wellbeing) a statistic that pushed the CDC to label sleep deprivation as a public health epidemic in the United States (Center for Disease Control, 2017; Howe, 2017).

What does this mean for workers? Not getting enough sleep impairs brain function, reducing a person’s ability to make rational judgements (Lewis, 2018), reducing worker productivity (Lewis, 2018), and even increasing chances of developing arthritis, depression, and suicide (Center for Disease Control, 2017). In addition, those who do not sleep the minimum required hours are at a greater risk for obesity, heart attacks, strokes, and diabetes (Center for Disease Control, 2017). Although a lack of sleep impairs the individual workers, these individual impairments are adding up and impacting the entire Japanese workplace, even extending to their economy. Rand Corporation conducted a study in 2009 to quantify the cost of insufficient sleep in Japanese workers (Lewis, 2018; Hafner, Stepanek, Taylor, Troxel, & Van Stolk, 2016). Their study suggested that Japan loses 138 billion US dollars per year (the equivalent of 2.92% of Japan’s gross domestic product) because of the higher mortality risks and productivity losses resulting from the sleep shortage (Lewis, 2018; Hafner, et al., 2016). According to Rand Corporation’s 2009 study, the United States is estimated to lose $411 billion a year because of problems associated with deficient sleep in workers (Lewis, 2018; Hafner, et al., 2016).

This worrisome change in the sleep patterns of workers holds major implications for modern organizations and economies. Fortunately, some companies are creating systems that reward and encourage workers to sleep. These attempts range from providing a raise for workers who sleep at least six hours a night, to installing rooms in the workplace dedicated to napping (Fleming, 2018). In addition, Japan’s Ministry of Health, Labor, and Welfare issued guidance for employers in how to encourage employees to sleep more, primarily through educating both employers and employees about the benefits of getting enough sleep (Fleming, 2018; Lewis, 2018). Moving forward, we suggest organizations take steps to further encourage employee health and wellbeing, including sleep hygiene.

References

Center for Disease Control. (2017). Short Sleep Duration Among US Adults. Retrieved from https://www.cdc.gov

Hafner, M., Stepanek, M., Taylor, J., Troxel, W. M., & Van Stolk, C. (2016). Why sleep matters — the economic costs of insufficient sleep: A cross-country comparative analysis. Retrieved from www.rand.org/giving/contribute

Howe, N. (2017, August 18). America The Sleep-Deprived. Retrieved from https://www.forbes.com

Lewis, L. (2018, November 19). Japan wakes up to sleep shortage problems. Retrieved from https://www.ft.com

Fleming, S. (2018, November 28). To combat Japan’s sleep debt, some firms allow tired workers to nap on the job. Retrieved from https://www.weforum.org

Network Research Highlight: Cyber-Vetting May Be Limiting Talent Pools

By: Elizabeth Moraff & Keaton Fletcher

A recent paper published by Debora Jeske, Sonia Lippke, and Work Science Center Network Member, Kenneth Shultz, in the Employee Responsibilities and Rights Journal, highlights the increasingly confusing role of social media in job selection. Cyber-vetting is a process in which employers screen potential employees based on information provided in their social media accounts and other online presences. However, the opportunity to reduce risk on the part of the employer, through cyber-vetting, may in fact, increase perceived risk for applicants, particularly those who have personal information that may impact their prospects of being hired. Willingness to disclose information, as well as privacy concerns, may very well be affecting what applicants complete the recruitment process. People vary in their willingness to share personal information with others, what the authors call, self-disclosure.

The researchers recruited over 200 undergraduates at a university in the U.K. and asked them to imagine themselves applying to various jobs, ranging from sales to government think tanks to childcare. In some of the conditions, participants were asked to provide their login information for all of their social media accounts, which the interviewer would use to peruse their accounts during an interview. Participants then indicated whether or not they intended to continue in the application process (Jeske et. al, 2019).

Participants who typically engaged in higher self-disclosure behavior were more willing to continue the application process, despite the need to share their social media information. The researchers also found that if participants felt as though the information from their social media accounts may be used inappropriately and if they were generally concerned about privacy, they were less likely to continue with the application. Applicants who felt vulnerable and were worried about a prospective employer invading their social media accounts were less likely to provide the requested information, and less likely to indicate they would persevere in the process. If an applicant did not feel vulnerable, though, their concern about global privacy did not affect their self-disclosure of information (Jeske et. al, 2019). Additionally, the study demonstrated that willingness to trust influenced self-disclosure independently. People who were more willing to trust an employer gave more information.

Moving forward, this suggests that employers who require applicants to share their social media account information for cyber-vetting may be limiting their applicant pool on traits that are not necessarily relevant to job performance (e.g., preference for privacy). These unexpected findings potentially serve as a caution to employers about the way they talk about social media screenings with applicants. Applicants who feel vulnerable, potentially those who carry stigmatized work identities, such as a disability, may be more likely to drop out of the recruitment process when it seems that an organization may seek sensitive information about them. The researchers suggest that companies might mitigate these potential effects by limiting themselves to asking for information from applicants that they truly need, and by clarifying for applicants exactly how they intend to glean information from social media, and to what end.

Building a Bridge from Fulltime Work to Retirement

By: Riley Swab

Although older workers contribute valuable ideas, knowledge, and experience to the workforce, these can often be overshadowed by their potential loss in innovative ideas and physical abilities (Zacher, Kooij, & Beier, 2018). A solution to this cost-benefit analysis may be bridge employment, which is a type of partial retirement taken between fulltime work and full retirement (Beehr, & Bennett, 2014). Bridge employment is similar to short-term work, with the hours being more flexible than part-time employment, but the end goal being full retirement in a relatively short amount of time. Bridge employment allows the workforce to take advantage of older workers’ benefits, while limiting the amount of time their disadvantages would negatively impact their working ability.

One benefit to bridge employment is that it offers older workers the chance to focus on mentoring younger workers, leveraging their increased experience and knowledge to help the workforce (Beehr, & Bennett, 2014). This also allows older workers to leave a tangible legacy behind. This mentorship keeps the older workers connected to the workforce even once they pass into full retirement, as it fosters an active interpersonal connection through the mentee.

Bridge employment also offers security in older workers’ identity. Beehr and Bennett (2014) found that an older worker’s occupation tends to be a source of identity or status, which is often lost during retirement or the later days of working. Bridge employment allows these older workers to ease into retirement by giving them time to find other sources of identity. Without bridge employment, many retired workers find themselves suddenly with no workplace or job to accomplish, causing them to quickly reevaluate their identity or status. Bridge employment, however, gives more time towards this transition, changing it from a rapid or sudden transition to a gradual transition.

Furthermore, bridge employment allows older workers who have not saved enough for retirement to supplement their retirement funds without committing to the hours of full-time employment (Beehr & Bennett, 2014). Increased retirement age is becoming increasingly more common as workers are forced to remain in the workforce because of a lack of money. This pressing need for money often overshadows the potential drawbacks of continuing to work full time as an older work (Beehr & Bennett, 2014). Bridge employment, however, allows older workers to continue making money without committing to the hours of full-time employment.

Bridge employment may be a viable option for encouraging active aging in the workforce by providing older workers the benefit of continued job satisfaction. Older workers’ identity or status is also often helped through bridge employment by providing them a more gradual transition out of the workforce, as opposed to going straight from full employment to full retirement. Bridge employment also benefits younger workers by providing them mentors with knowledge and experience. Although current studies have researched the reasoning behind bridge employment, the outcome of bridge employment is still an area that needs to be better researched.

References

Beehr, T. A., & Bennett, M.M, 2014. Working After Retirement: Features of Bridge Employment and Research Directions. Work, Aging and Retirement, 1(1), 112-128. http://doi.org/10.1093/workar/wau007

Zacher, H., Koiij, D.T.A.M., & Beier, M.E. (2018). Active aging at work: Contributing factors and implications for organizations. Organizational Dynamics, 47(1), 37-45. https://doi.org/10.1016/j.orgdyn.2017.08.001

Network Research Highlight: Creating Enriched Jobs

By: Elizabeth Moraff

An enriched work design is one in which work roles provide employees with autonomy, task variety, and opportunities to use and develop skills. Despite a wealth of literature pointing towards the benefits of enriched work design, low-quality and poorly-designed jobs continue to pervade the global workspace (Parker, Andrei, & Broeck 2019). Further, relatively little research examines the variables that affect what strategies people use when designing jobs (Parker, Andrei, & Broeck 2019). Work Science Center Network member Sharon K. Parker, with Daniela M. Andrei and Anja den Broeck, sought to ameliorate this gap in a study published in the Journal of Applied Psychology.

Parker and colleagues proposed that supervisors have a tendency towards creating simplified roles while designing work, and that this may lead to the low proportion of enriched work roles. Indeed, they replicated the findings of a 1991 study (Campion & Stevens), in which undergraduate students, when given the opportunity to design clerical roles, overwhelmingly utilized strategies geared towards efficiency and role simplicity, rather than enrichment and enjoyability for workers.

Given this consistent finding, the researchers designed two subsequent studies to explain some of the factors that affect why people choose particular strategies when designing work.

The studies indicated a few important influences on what factors led to more enriched work designs. Firstly, a worker’s current experience of job autonomy corresponded to an increased tendency to design enriching roles for others (Parker, Andrei, & Broeck 2019). Secondly, although registered I-O Psychologists were also more likely to create more enriched roles, this inclination likely stemmed from work experience, which bred implicit knowledge and practical skills, rather than explicit knowledge from their training (Parker, Andrei, & Broeck 2019). In other words, even though I-O Psychologists are experts in work design, they used more enriching strategies not because they were specifically taught to, but because they had experiences that promoted this behavior. Further, openness to change did positively correlate with more enriching strategies and task allocation. Participants who ranked lower on openness to change tended to design less enriched roles (Parker, Andrei, & Broeck 2019).

We are excited for the implications these findings have for IO Psychologists, particularly those involved in designing work and influencing the processes in which work roles emerge. As the Work Science Center, we are glad to highlight research that builds our understanding of how to equip people to design more enriching work.

References

Campion, M. A., & Stevens, M. J. (1991). Neglected questions in job design: How people design jobs, task-job predictability, and influence of training. Journal of Business and Psychology, 6, 169 –191. http://dx.doi .org/10.1007/BF01126707

Parker, S. K., Andrei, D. M., & Van den Broeck, A. (2019). Poor work design begets poor work design: Capacity and willingness antecedents of individual work design behavior. Journal of Applied Psychology. Advance online publication. http://dx.doi.org/10.1037/apl0000383

Healthcare Goes High-Tech

By: Catherine Liu

Modern healthcare organizations are adapting and innovating in response to the boom in artificial intelligence. A recent paper details two distinct branches of use for artificial intelligence in healthcare: virtual and physical.

The virtual branch encompasses the use of deep learning in information management, management of electronic health records, and guidance of physicians in decision making. The virtual branch focuses on technology that can assist healthcare workers by processing and organizing information so less time is spent on menial tasks that could be completed by a computer. For example, electronic medical records make patient information easily accessible to doctors and nurses and allow for important information to be collectively organized in one location. The virtual branch also includes the many applications of machine learning techniques to imaging technology used by radiologists.

In contrast to the virtual branch, the physical branch focuses on tangible technologies that capitalize upon artificial intelligence in order to complete a set of tasks. This can include nanorobots that assist with drug delivery and robots that are used to assist elderly patients. For example, human-interactive robots can provide assistance, guide, and assist with psychological-enrichment with older patients (Shibata et al., 2010).

Although artificial intelligence holds great promise, there is a myriad of societal and ethical complexities that result from the use of artificial intelligence in healthcare, given concerns over reliability, safety, and accountability. As detailed at the Nuffield Council on Bioethics, artificial intelligence currently has many limitations in the medical field. For example, artificial intelligence is reliant on large amounts of data in order to learn how to behave, but the current availability and quality of medical data may not be sufficient for this purpose. Artificial intelligence may also propagate inequalities in healthcare if trained on biased data and may negatively affect patients. For example, a recent study found that men and women receive different treatment after heart attacks. Thus, if the training data did not account for this difference and included primarily male patients, the treatment suggestions given by the artificial intelligence program would be biased and thus may negatively affect female patients. On a practical note, artificial intelligence is limited by computing power, so the large, complex datasets inherent to healthcare may present a challenge, particularly for those organizations that do not have the financial resources to purchase and maintain computers capable of these calculations. Lastly, artificially intelligent systems may lack the empathy or ability to process a complex situation in order to ensure the correct suggestions for what further treatments should be pursued, as in the case of palliative care.

Rather than using artificial intelligence independently or completely abandoning it, combining the predictions made from machine learning algorithms with the expertise and empathy of healthcare providers may allow for better, more comprehensive treatment overall as we head into the future of modern healthcare.