Feeling the Burn? 10 Ways Healthcare Providers Can Proactively Address Burnout

Burnout in healthcare is rampant, and it is not limited to one clinical setting or a particular type of provider. Rather, feelings of exhaustion, cynicism, pessimism, detachment, and ineffectiveness can take a grip on healthcare providers of various ages, backgrounds, and specialties and have far-reaching consequences.

Five Ways Healthcare Organizations Can Confront Burnout and Build Cultures of Well-Being and Resiliency

The impact of organizational culture on productivity, morale, staff retention, patient outcomes, safety, security, and overall well-being in healthcare is profound. A toxic culture can enable or even encourage a range of inappropriate and harmful behaviors, such as bullying, sexual harassment, and abuses of power. In these situations, the cultural deficiencies and their consequences might be conspicuous. In other circumstances, though, cultural problems might not be toxic in an overt way. Rather, more discreet issues — such as suboptimal processes, workload fatigue, loss of autonomy, and work–life imbalance — can compound to produce a pernicious effect, such as clinician and staff burnout.

Addressing Racial Disparity in Maternal Health

One of the key challenges in addressing maternal mortality is the racial disparity that exists. Among high-resource countries, the United States has the highest rate of maternal mortality, and the risk is three to four times higher for black women, according to the Institute for Healthcare Improvement.¹

Some contributing factors to the racial disparity include unconscious biases — both institutional and structural racism — and lack of insurance.

Strategies to Prevent Maternal Morbidity and Mortality

An increasing number of pregnant women in the United States have chronic health conditions, such as hypertension, diabetes, and chronic heart disease, which put them at a higher risk of pregnancy complications.When combined with hemorrhage, cardiovascular disease, sepsis, and other health problems, these conditions have been responsible for a large number of pregnancy-related deaths in the United States.

15 Strategies for Tackling the Top Malpractice Allegation in Gynecology

MedPro Group data show that allegations related to surgical treatment represent the largest claims category for gynecology providers (67 percent of all gynecology claims closed between 2008 and 2017). Surgical treatment allegations also account for almost two-thirds of all dollars paid for expense and indemnity costs associated with gynecology claims.

Artificial Intelligence and Informed Consent

Informed consent, in its basic sense, seems like a fairly straightforward concept. A patient is informed about a proposed test, treatment, or procedure; its benefits and risks; and any alternative options. With this knowledge, the patient decides to either consent or not consent to the recommended plan. In reality, though, informed consent is a more complex process that involves nondelegable duties and varies in scope based on the type of test, treatment, or procedure involved.

When technology is introduced into the mix — particularly advanced technology — the informed consent process can become even more complicated because of additional information that the provider must convey to the patient and that the patient must weigh in his/her decision-making process.

Waiting for Watson: Challenges and Risks in Using Artificial Intelligence in Healthcare

Artificial intelligence, or AI, is a burgeoning field in health information technology and a key element in envisioning the future of healthcare. Daily stories trend in the media related to AI applications and their widespread potential for revolutionizing medical practice and patient care. Yet, akin to the promises of electronic health records (EHRs) in the early 21st century, the excitement surrounding AI has no doubt led to a sensationalized view of its capabilities while marginalizing technological and operational challenges as well as safety and ethical concerns.²

Artificial Intelligence Risks: Biased Data and Functional Issues

One of the major red flags raised about artificial intelligence (AI) is the potential for bias in the data on which machines are trained and — as a result — bias in their algorithms. Bias can occur for various reasons. For example, the data itself might be biased; research has shown racial, gender, socioeconomic, and age-related disparities in medical studies. If machines are trained on data from these studies, their algorithms will reflect that bias, perpetuating the problem and potentially leading to suboptimal recommendations and patient outcomes.¹