Practice Management

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; [email protected]

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; [email protected]

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; [email protected]

Disclosures: None reported.

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; [email protected]

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; [email protected]

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; [email protected]

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

AURORA, COLO. – Artificial Intelligence has arrived at medical offices, whether or not clinicians feel ready for it.

AI might result in more accurate, efficient, and cost-effective care. But it’s possible it could cause harm. That’s according to Benjamin Collins, MD, at Vanderbilt University Medical Center, Nashville, Tenn., who spoke on the subject at the annual meeting of the Society of General Internal Medicine.

Understanding the nuances of AI is even more important because of the quick development of the algorithms.

“When I submitted this workshop, there was no ChatGPT,” said Dr. Collins, referring to Chat Generative Pre-trained Transformer, a recently released natural language processing model. “A lot has already changed.”
 

Biased data

Biased data are perhaps the biggest pitfall of AI algorithms, Dr. Collins said. If garbage data go in, garbage predictions come out.

If the dataset that trains the algorithm underrepresents a particular gender or ethnic group, for example, the algorithm may not respond accurately to prompts. When an AI tool compounds existing inequalities related to socioeconomic status, ethnicity, or sexual orientation, the algorithm is biased, according to Harvard researchers.

“People often assume that artificial intelligence is free of bias due to the use of scientific processes and its development,” he said. “But whatever flaws exist in data collection and old data can lead to poor representation or underrepresentation in the data used to train the AI tool.”

Racial minorities are underrepresented in studies; therefore, data input into an AI tool might skew results for these patients.

The Framingham Heart Study, for example, which began in 1948, examined heart disease in mainly White participants. The findings from the study resulted in the creation of a sex-specific algorithm that was used to estimate the 10-year cardiovascular risk of a patient. While the cardiovascular risk score was accurate for White persons, it was less accurate for Black patients.

A study published in Science in 2019 revealed bias in an algorithm that used health care costs as a proxy for health needs. Because less money was spent on Black patients who had the same level of need as their White counterparts, the output inaccurately showed that Black patients were healthier and thus did not require extra care.

Developers can also be a source of bias, inasmuch as AI often reflects preexisting human biases, Dr. Collins said.

“Algorithmic bias presents a clear risk of harm that clinicians must play against the benefits of using AI,” Dr. Collins said. “That risk of harm is often disproportionately distributed to marginalized populations.”

As clinicians use AI algorithms to diagnose and detect disease, predict outcomes, and guide treatment, trouble comes when those algorithms perform well for some patients and poorly for others. This gap can exacerbate existing disparities in health care outcomes.

Dr. Collins advised clinicians to push to find out what data were used to train AI algorithms to determine how bias could have influenced the model and whether the developers risk-adjusted for bias. If the training data are not available, clinicians should ask their employers and AI developers to know more about the system.

Clinicians may face the so-called black box phenomenon, which occurs when developers cannot or will not explain what data went into an AI model, Dr. Collins said.

According to Stanford (Calif.) University, AI must be trained on large datasets of images that have been annotated by human experts. Those datasets can cost millions of dollars to create, meaning corporations often fund them and do not always share the data publicly.

Some groups, such as Stanford’s Center for Artificial Intelligence in Medicine and Imaging, are working to acquire annotated datasets so researchers who train AI models can know where the data came from.

Paul Haidet, MD, MPH, an internist at Penn State College of Medicine, Hershey, sees the technology as a tool that requires careful handling.

“It takes a while to learn how to use a stethoscope, and AI is like that,” Dr. Haidet said. “The thing about AI, though, is that it can be just dropped into a system and no one knows how it works.”

Dr. Haidet said he likes knowing how the sausage is made, something AI developers are often reticent to make known.

“If you’re just putting blind faith in a tool, that’s scary,” Dr. Haidet said.
 

Transparency and ‘explainability’

The ability to explain what goes into tools is essential to maintaining trust in the health care system, Dr. Collins said.

“Part of knowing how much trust to place in the system is the transparency of those systems and the ability to audit how well the algorithm is performing,” Dr. Collins said. “The system should also regularly report to users the level of certainty with which it is providing an output rather than providing a simple binary output.”

Dr. Collins recommends that providers develop an understanding of the limits of AI regulations as well, which might including learning how the system was approved and how it is monitored.

“The FDA has oversight over some applications of AI and health care for software as a medical device, but there’s currently no dedicated process to evaluate the systems for the presence of bias,” Dr. Collins said. “The gaps in regulation leave the door open for the use of AI in clinical care that contain significant biases.”

Dr. Haidet likened AI tools to the Global Positioning System: A good GPS system will let users see alternate routes, opt out of toll roads or highways, and will highlight why routes have changed. But users need to understand how to read the map so they can tell when something seems amiss.

Dr. Collins and Dr. Haidet report no relevant financial relationships

A version of this article first appeared on Medscape.com.

AURORA, COLO. – Artificial Intelligence has arrived at medical offices, whether or not clinicians feel ready for it.

AI might result in more accurate, efficient, and cost-effective care. But it’s possible it could cause harm. That’s according to Benjamin Collins, MD, at Vanderbilt University Medical Center, Nashville, Tenn., who spoke on the subject at the annual meeting of the Society of General Internal Medicine.

Understanding the nuances of AI is even more important because of the quick development of the algorithms.

“When I submitted this workshop, there was no ChatGPT,” said Dr. Collins, referring to Chat Generative Pre-trained Transformer, a recently released natural language processing model. “A lot has already changed.”
 

Biased data

Biased data are perhaps the biggest pitfall of AI algorithms, Dr. Collins said. If garbage data go in, garbage predictions come out.

If the dataset that trains the algorithm underrepresents a particular gender or ethnic group, for example, the algorithm may not respond accurately to prompts. When an AI tool compounds existing inequalities related to socioeconomic status, ethnicity, or sexual orientation, the algorithm is biased, according to Harvard researchers.

“People often assume that artificial intelligence is free of bias due to the use of scientific processes and its development,” he said. “But whatever flaws exist in data collection and old data can lead to poor representation or underrepresentation in the data used to train the AI tool.”

Racial minorities are underrepresented in studies; therefore, data input into an AI tool might skew results for these patients.

The Framingham Heart Study, for example, which began in 1948, examined heart disease in mainly White participants. The findings from the study resulted in the creation of a sex-specific algorithm that was used to estimate the 10-year cardiovascular risk of a patient. While the cardiovascular risk score was accurate for White persons, it was less accurate for Black patients.

A study published in Science in 2019 revealed bias in an algorithm that used health care costs as a proxy for health needs. Because less money was spent on Black patients who had the same level of need as their White counterparts, the output inaccurately showed that Black patients were healthier and thus did not require extra care.

Developers can also be a source of bias, inasmuch as AI often reflects preexisting human biases, Dr. Collins said.

“Algorithmic bias presents a clear risk of harm that clinicians must play against the benefits of using AI,” Dr. Collins said. “That risk of harm is often disproportionately distributed to marginalized populations.”

As clinicians use AI algorithms to diagnose and detect disease, predict outcomes, and guide treatment, trouble comes when those algorithms perform well for some patients and poorly for others. This gap can exacerbate existing disparities in health care outcomes.

Dr. Collins advised clinicians to push to find out what data were used to train AI algorithms to determine how bias could have influenced the model and whether the developers risk-adjusted for bias. If the training data are not available, clinicians should ask their employers and AI developers to know more about the system.

Clinicians may face the so-called black box phenomenon, which occurs when developers cannot or will not explain what data went into an AI model, Dr. Collins said.

According to Stanford (Calif.) University, AI must be trained on large datasets of images that have been annotated by human experts. Those datasets can cost millions of dollars to create, meaning corporations often fund them and do not always share the data publicly.

Some groups, such as Stanford’s Center for Artificial Intelligence in Medicine and Imaging, are working to acquire annotated datasets so researchers who train AI models can know where the data came from.

Paul Haidet, MD, MPH, an internist at Penn State College of Medicine, Hershey, sees the technology as a tool that requires careful handling.

“It takes a while to learn how to use a stethoscope, and AI is like that,” Dr. Haidet said. “The thing about AI, though, is that it can be just dropped into a system and no one knows how it works.”

Dr. Haidet said he likes knowing how the sausage is made, something AI developers are often reticent to make known.

“If you’re just putting blind faith in a tool, that’s scary,” Dr. Haidet said.
 

Transparency and ‘explainability’

The ability to explain what goes into tools is essential to maintaining trust in the health care system, Dr. Collins said.

“Part of knowing how much trust to place in the system is the transparency of those systems and the ability to audit how well the algorithm is performing,” Dr. Collins said. “The system should also regularly report to users the level of certainty with which it is providing an output rather than providing a simple binary output.”

Dr. Collins recommends that providers develop an understanding of the limits of AI regulations as well, which might including learning how the system was approved and how it is monitored.

“The FDA has oversight over some applications of AI and health care for software as a medical device, but there’s currently no dedicated process to evaluate the systems for the presence of bias,” Dr. Collins said. “The gaps in regulation leave the door open for the use of AI in clinical care that contain significant biases.”

Dr. Haidet likened AI tools to the Global Positioning System: A good GPS system will let users see alternate routes, opt out of toll roads or highways, and will highlight why routes have changed. But users need to understand how to read the map so they can tell when something seems amiss.

Dr. Collins and Dr. Haidet report no relevant financial relationships

A version of this article first appeared on Medscape.com.

AURORA, COLO. – Artificial Intelligence has arrived at medical offices, whether or not clinicians feel ready for it.

AI might result in more accurate, efficient, and cost-effective care. But it’s possible it could cause harm. That’s according to Benjamin Collins, MD, at Vanderbilt University Medical Center, Nashville, Tenn., who spoke on the subject at the annual meeting of the Society of General Internal Medicine.

Understanding the nuances of AI is even more important because of the quick development of the algorithms.

“When I submitted this workshop, there was no ChatGPT,” said Dr. Collins, referring to Chat Generative Pre-trained Transformer, a recently released natural language processing model. “A lot has already changed.”
 

Biased data

Biased data are perhaps the biggest pitfall of AI algorithms, Dr. Collins said. If garbage data go in, garbage predictions come out.

If the dataset that trains the algorithm underrepresents a particular gender or ethnic group, for example, the algorithm may not respond accurately to prompts. When an AI tool compounds existing inequalities related to socioeconomic status, ethnicity, or sexual orientation, the algorithm is biased, according to Harvard researchers.

“People often assume that artificial intelligence is free of bias due to the use of scientific processes and its development,” he said. “But whatever flaws exist in data collection and old data can lead to poor representation or underrepresentation in the data used to train the AI tool.”

Racial minorities are underrepresented in studies; therefore, data input into an AI tool might skew results for these patients.

The Framingham Heart Study, for example, which began in 1948, examined heart disease in mainly White participants. The findings from the study resulted in the creation of a sex-specific algorithm that was used to estimate the 10-year cardiovascular risk of a patient. While the cardiovascular risk score was accurate for White persons, it was less accurate for Black patients.

A study published in Science in 2019 revealed bias in an algorithm that used health care costs as a proxy for health needs. Because less money was spent on Black patients who had the same level of need as their White counterparts, the output inaccurately showed that Black patients were healthier and thus did not require extra care.

Developers can also be a source of bias, inasmuch as AI often reflects preexisting human biases, Dr. Collins said.

“Algorithmic bias presents a clear risk of harm that clinicians must play against the benefits of using AI,” Dr. Collins said. “That risk of harm is often disproportionately distributed to marginalized populations.”

As clinicians use AI algorithms to diagnose and detect disease, predict outcomes, and guide treatment, trouble comes when those algorithms perform well for some patients and poorly for others. This gap can exacerbate existing disparities in health care outcomes.

Dr. Collins advised clinicians to push to find out what data were used to train AI algorithms to determine how bias could have influenced the model and whether the developers risk-adjusted for bias. If the training data are not available, clinicians should ask their employers and AI developers to know more about the system.

Clinicians may face the so-called black box phenomenon, which occurs when developers cannot or will not explain what data went into an AI model, Dr. Collins said.

According to Stanford (Calif.) University, AI must be trained on large datasets of images that have been annotated by human experts. Those datasets can cost millions of dollars to create, meaning corporations often fund them and do not always share the data publicly.

Some groups, such as Stanford’s Center for Artificial Intelligence in Medicine and Imaging, are working to acquire annotated datasets so researchers who train AI models can know where the data came from.

Paul Haidet, MD, MPH, an internist at Penn State College of Medicine, Hershey, sees the technology as a tool that requires careful handling.

“It takes a while to learn how to use a stethoscope, and AI is like that,” Dr. Haidet said. “The thing about AI, though, is that it can be just dropped into a system and no one knows how it works.”

Dr. Haidet said he likes knowing how the sausage is made, something AI developers are often reticent to make known.

“If you’re just putting blind faith in a tool, that’s scary,” Dr. Haidet said.
 

Transparency and ‘explainability’

The ability to explain what goes into tools is essential to maintaining trust in the health care system, Dr. Collins said.

“Part of knowing how much trust to place in the system is the transparency of those systems and the ability to audit how well the algorithm is performing,” Dr. Collins said. “The system should also regularly report to users the level of certainty with which it is providing an output rather than providing a simple binary output.”

Dr. Collins recommends that providers develop an understanding of the limits of AI regulations as well, which might including learning how the system was approved and how it is monitored.

“The FDA has oversight over some applications of AI and health care for software as a medical device, but there’s currently no dedicated process to evaluate the systems for the presence of bias,” Dr. Collins said. “The gaps in regulation leave the door open for the use of AI in clinical care that contain significant biases.”

Dr. Haidet likened AI tools to the Global Positioning System: A good GPS system will let users see alternate routes, opt out of toll roads or highways, and will highlight why routes have changed. But users need to understand how to read the map so they can tell when something seems amiss.

Dr. Collins and Dr. Haidet report no relevant financial relationships

A version of this article first appeared on Medscape.com.

Many of the persistent problems that breast cancer survivors experience after treatment are not part of the conversation with oncologists during treatment, said Patricia A. Ganz, MD, during a presentation at the European Society for Medical Oncology Breast Cancer annual congress.

Several studies suggest that many breast cancer patients are not well prepared to move forward after a breast cancer diagnosis and subsequent treatments, continued Dr. Ganz, who works at the UCLA Jonsson Comprehensive Cancer Center, Los Angeles.

Meeting the survivorship needs of breast cancer patients requires addressing both their physical and psychosocial needs, Dr. Ganz said. She explained how to achieve that, but first pointed to research elaborating on what's missing from some breast cancer survivors' care and barriers to these patients having their variety of health-related needs met.

In a 2021 study published in the Journal of Cancer Survivorship, Dr. Ganz and colleagues conducted a survey of approximately 200 medical oncologists in the United States. They determined that less than 50% provide survivorship care plans to patients at the end of treatment or communicate with patients’ other physicians about follow-up care.

In a secondary analysis of data from the same survey published in 2022 in Breast Cancer Research and Treatment, Dr. Ganz and colleagues examined medical oncologists’ perceived barriers to addressing both physical and psychosocial long-term effects in breast cancer survivors. For both, lack of time was the greatest perceived barrier, cited by nearly two-thirds of oncologists. Other barriers to addressing physical effects included lack of evidence-based, effective interventions, lack of clinical algorithms to guide care, and ambiguity regarding professional responsibility at the end of treatment. Other top barriers to addressing psychosocial issues included lack of mental health providers, lack of psychosocial resources, and lack of clinician knowledge and skills.

Data from additional studies suggest that, overall, cancer patients with greater physical burdens, such as more complex and lengthy treatment regimens, also have greater psychosocial needs, Dr. Ganz noted. Plus, approximately 15%-20% of cancer survivors have ongoing anxiety and depressive symptoms.

Shift to primary care

As more breast cancer and other cancer patients survive for longer periods, more care will likely occur in general medical settings, Dr. Ganz said. Issues to be addressed will include the potential increased risk of comorbid conditions for these survivors, and whether survivorship interventions earlier in the disease trajectory will impact survivorship. For cancer patients who achieve remission after treatment, the first 5 years after a diagnosis involves treatment and short-term surveillance for late effects. Beyond 5 years, care for cancer survivors mainly involves primary care and management of any comorbid conditions, as well as surveillance for late effects and recurrences, and awareness of new research.

A patient consultation early in the process after diagnosis is the start of a continuum of care, Dr. Ganz said. A patient consultation should address symptoms related to initial treatments, such as neuropathy, pain, fatigue, and insomnia, as well as the psychological symptoms of anxiety and depression. An early consultation also should evaluate adherence to endocrine therapy and management of symptoms, if needed, with the larger goal of preparing patients for recovery and the transition to survivorship, and what to expect for long-term follow-up.
 

Delivering the three P’s

The “Three P’s” of survivor care for breast cancer patients are palliation, prevention, and promotion of health, according to Dr. Ganz .

The first “P,” for palliative, is a key part of survivorship care, said Dr. Ganz. Palliative care is defined as care that focuses on reducing symptom severity and improving quality of life. The biological effects of cancer treatment can be associated with physical effects, such as functional limitations and frailty, and behavioral/cognitive effects such as depression, fatigue, and cognitive deficits, she said. To manage these effects and provide palliative care, consultation is needed with specialists in relevant areas including mental health, pain management, physical medicine/rehabilitation, endocrinology, cardiology, and neurology.

The second “P,” which is for prevention in survivorship care, refers to ongoing follow-up screening to identify any potentially serious late-onset complications such as osteoporosis or cardiac disease so they can be addressed, said Dr. Ganz. Other considerations include chemoprevention if available and genetic counseling for patients with hereditary cancers. Prevention also includes counseling patients about lifestyle modifications to help prevent additional cancer.

The goal of the third “P,” which is for health promotion, is to promote risk reduction for the health problems associated with accelerated aging that may arise in cancer survivors, said Dr. Ganz.

Health promotion strategies include maintaining a healthy weight, increasing physical activity, and avoiding harmful exposures, she said. Healthy lifestyle interventions can also reduce the risk of other chronic diseases such as diabetes and heart disease.

To that end, Dr. Ganz outlined several behavioral interventions that may mitigate the effects of cancer treatment on the accelerated aging process, including stress reduction in the form of meditation or yoga, cognitive behavioral therapy, improving sleep, increasing physical activity, reducing obesity, and decreasing tobacco and alcohol use. These interventions may help reduce inflammation and promote tissue repair and healing.

For cancer survivors, the life span may be longer than the health span, and these patients may benefit from an integrated model of care, with systematic screening and consolidated appointments, rather than a fragmented model in which departments and referrals are siloed, which may result in conflicting advice or redundancy, said Dr. Ganz.

Looking ahead, more research is needed to explore models of care delivery, as requirements for survivor care will vary among patients and care settings, Dr. Ganz said.

However, regardless of setting, treatment plans and shared decision-making can help reduce potential long-term or late-emerging effects, she said. Developing a survivorship care plan can help patients learn how to enhance their recovery.

During a question and answer session, Dr. Ganz was asked about whether hormone therapy could be used for patients with hormone negative breast cancer. “I think vaginal estrogen can be used if someone is on tamoxifen,” she said. However, “we need to be cautious” in case there are remaining estrogen positive cells, in order to avoid potential metastases, and use of hormone therapy in breast cancer survivors is an individualized decision based in part on quality of life.
 

Engaging a patient’s partner early can be helpful

If possible, engage the patient’s partner in survivorship discussions, said Luzia Travado, PhD, head of psycho-oncology at the Champalimaud Foundation, Lisbon, who presented on the topic of sexuality and commented on survivorship during the discussion. For those women with partners, engaging the partner early in treatment often means they are more likely to play a larger role in the post treatment and long term by providing stability and emotional support.

“Make sure partners are engaged and understand that they have a role, and that this role is valued,” she said. Unfortunately, there are a lot of divorced women with breast cancer, as the disease can take a toll on relationships. However, remember “sexuality is not just sex; it is caring, loving, and intimacy.”

“To end on a positive note, it is important to empower patients, and to give them self-management skills so they can make things even better in their survivorship,” said Dr. Ganz. In spite of discussing difficulties and challenges, one of the goals of the session was to offer potential solutions and answers.

Dr. Ganz disclosed serving as editor of the cancer survivorship section on Up-to-Date, and serving as a consultant for Blue Note Therapeutics, GRAIL, InformedDNA, and Roche-Genentech. Dr. Travado had no relevant financial conflicts to disclose.

Many of the persistent problems that breast cancer survivors experience after treatment are not part of the conversation with oncologists during treatment, said Patricia A. Ganz, MD, during a presentation at the European Society for Medical Oncology Breast Cancer annual congress.

Several studies suggest that many breast cancer patients are not well prepared to move forward after a breast cancer diagnosis and subsequent treatments, continued Dr. Ganz, who works at the UCLA Jonsson Comprehensive Cancer Center, Los Angeles.

Meeting the survivorship needs of breast cancer patients requires addressing both their physical and psychosocial needs, Dr. Ganz said. She explained how to achieve that, but first pointed to research elaborating on what's missing from some breast cancer survivors' care and barriers to these patients having their variety of health-related needs met.

In a 2021 study published in the Journal of Cancer Survivorship, Dr. Ganz and colleagues conducted a survey of approximately 200 medical oncologists in the United States. They determined that less than 50% provide survivorship care plans to patients at the end of treatment or communicate with patients’ other physicians about follow-up care.

In a secondary analysis of data from the same survey published in 2022 in Breast Cancer Research and Treatment, Dr. Ganz and colleagues examined medical oncologists’ perceived barriers to addressing both physical and psychosocial long-term effects in breast cancer survivors. For both, lack of time was the greatest perceived barrier, cited by nearly two-thirds of oncologists. Other barriers to addressing physical effects included lack of evidence-based, effective interventions, lack of clinical algorithms to guide care, and ambiguity regarding professional responsibility at the end of treatment. Other top barriers to addressing psychosocial issues included lack of mental health providers, lack of psychosocial resources, and lack of clinician knowledge and skills.

Data from additional studies suggest that, overall, cancer patients with greater physical burdens, such as more complex and lengthy treatment regimens, also have greater psychosocial needs, Dr. Ganz noted. Plus, approximately 15%-20% of cancer survivors have ongoing anxiety and depressive symptoms.

Shift to primary care

As more breast cancer and other cancer patients survive for longer periods, more care will likely occur in general medical settings, Dr. Ganz said. Issues to be addressed will include the potential increased risk of comorbid conditions for these survivors, and whether survivorship interventions earlier in the disease trajectory will impact survivorship. For cancer patients who achieve remission after treatment, the first 5 years after a diagnosis involves treatment and short-term surveillance for late effects. Beyond 5 years, care for cancer survivors mainly involves primary care and management of any comorbid conditions, as well as surveillance for late effects and recurrences, and awareness of new research.

A patient consultation early in the process after diagnosis is the start of a continuum of care, Dr. Ganz said. A patient consultation should address symptoms related to initial treatments, such as neuropathy, pain, fatigue, and insomnia, as well as the psychological symptoms of anxiety and depression. An early consultation also should evaluate adherence to endocrine therapy and management of symptoms, if needed, with the larger goal of preparing patients for recovery and the transition to survivorship, and what to expect for long-term follow-up.
 

Delivering the three P’s

The “Three P’s” of survivor care for breast cancer patients are palliation, prevention, and promotion of health, according to Dr. Ganz .

The first “P,” for palliative, is a key part of survivorship care, said Dr. Ganz. Palliative care is defined as care that focuses on reducing symptom severity and improving quality of life. The biological effects of cancer treatment can be associated with physical effects, such as functional limitations and frailty, and behavioral/cognitive effects such as depression, fatigue, and cognitive deficits, she said. To manage these effects and provide palliative care, consultation is needed with specialists in relevant areas including mental health, pain management, physical medicine/rehabilitation, endocrinology, cardiology, and neurology.

The second “P,” which is for prevention in survivorship care, refers to ongoing follow-up screening to identify any potentially serious late-onset complications such as osteoporosis or cardiac disease so they can be addressed, said Dr. Ganz. Other considerations include chemoprevention if available and genetic counseling for patients with hereditary cancers. Prevention also includes counseling patients about lifestyle modifications to help prevent additional cancer.

The goal of the third “P,” which is for health promotion, is to promote risk reduction for the health problems associated with accelerated aging that may arise in cancer survivors, said Dr. Ganz.

Health promotion strategies include maintaining a healthy weight, increasing physical activity, and avoiding harmful exposures, she said. Healthy lifestyle interventions can also reduce the risk of other chronic diseases such as diabetes and heart disease.

To that end, Dr. Ganz outlined several behavioral interventions that may mitigate the effects of cancer treatment on the accelerated aging process, including stress reduction in the form of meditation or yoga, cognitive behavioral therapy, improving sleep, increasing physical activity, reducing obesity, and decreasing tobacco and alcohol use. These interventions may help reduce inflammation and promote tissue repair and healing.

For cancer survivors, the life span may be longer than the health span, and these patients may benefit from an integrated model of care, with systematic screening and consolidated appointments, rather than a fragmented model in which departments and referrals are siloed, which may result in conflicting advice or redundancy, said Dr. Ganz.

Looking ahead, more research is needed to explore models of care delivery, as requirements for survivor care will vary among patients and care settings, Dr. Ganz said.

However, regardless of setting, treatment plans and shared decision-making can help reduce potential long-term or late-emerging effects, she said. Developing a survivorship care plan can help patients learn how to enhance their recovery.

During a question and answer session, Dr. Ganz was asked about whether hormone therapy could be used for patients with hormone negative breast cancer. “I think vaginal estrogen can be used if someone is on tamoxifen,” she said. However, “we need to be cautious” in case there are remaining estrogen positive cells, in order to avoid potential metastases, and use of hormone therapy in breast cancer survivors is an individualized decision based in part on quality of life.
 

Engaging a patient’s partner early can be helpful

If possible, engage the patient’s partner in survivorship discussions, said Luzia Travado, PhD, head of psycho-oncology at the Champalimaud Foundation, Lisbon, who presented on the topic of sexuality and commented on survivorship during the discussion. For those women with partners, engaging the partner early in treatment often means they are more likely to play a larger role in the post treatment and long term by providing stability and emotional support.

“Make sure partners are engaged and understand that they have a role, and that this role is valued,” she said. Unfortunately, there are a lot of divorced women with breast cancer, as the disease can take a toll on relationships. However, remember “sexuality is not just sex; it is caring, loving, and intimacy.”

“To end on a positive note, it is important to empower patients, and to give them self-management skills so they can make things even better in their survivorship,” said Dr. Ganz. In spite of discussing difficulties and challenges, one of the goals of the session was to offer potential solutions and answers.

Dr. Ganz disclosed serving as editor of the cancer survivorship section on Up-to-Date, and serving as a consultant for Blue Note Therapeutics, GRAIL, InformedDNA, and Roche-Genentech. Dr. Travado had no relevant financial conflicts to disclose.

Many of the persistent problems that breast cancer survivors experience after treatment are not part of the conversation with oncologists during treatment, said Patricia A. Ganz, MD, during a presentation at the European Society for Medical Oncology Breast Cancer annual congress.

Several studies suggest that many breast cancer patients are not well prepared to move forward after a breast cancer diagnosis and subsequent treatments, continued Dr. Ganz, who works at the UCLA Jonsson Comprehensive Cancer Center, Los Angeles.

Meeting the survivorship needs of breast cancer patients requires addressing both their physical and psychosocial needs, Dr. Ganz said. She explained how to achieve that, but first pointed to research elaborating on what's missing from some breast cancer survivors' care and barriers to these patients having their variety of health-related needs met.

In a 2021 study published in the Journal of Cancer Survivorship, Dr. Ganz and colleagues conducted a survey of approximately 200 medical oncologists in the United States. They determined that less than 50% provide survivorship care plans to patients at the end of treatment or communicate with patients’ other physicians about follow-up care.

In a secondary analysis of data from the same survey published in 2022 in Breast Cancer Research and Treatment, Dr. Ganz and colleagues examined medical oncologists’ perceived barriers to addressing both physical and psychosocial long-term effects in breast cancer survivors. For both, lack of time was the greatest perceived barrier, cited by nearly two-thirds of oncologists. Other barriers to addressing physical effects included lack of evidence-based, effective interventions, lack of clinical algorithms to guide care, and ambiguity regarding professional responsibility at the end of treatment. Other top barriers to addressing psychosocial issues included lack of mental health providers, lack of psychosocial resources, and lack of clinician knowledge and skills.

Data from additional studies suggest that, overall, cancer patients with greater physical burdens, such as more complex and lengthy treatment regimens, also have greater psychosocial needs, Dr. Ganz noted. Plus, approximately 15%-20% of cancer survivors have ongoing anxiety and depressive symptoms.

Shift to primary care

As more breast cancer and other cancer patients survive for longer periods, more care will likely occur in general medical settings, Dr. Ganz said. Issues to be addressed will include the potential increased risk of comorbid conditions for these survivors, and whether survivorship interventions earlier in the disease trajectory will impact survivorship. For cancer patients who achieve remission after treatment, the first 5 years after a diagnosis involves treatment and short-term surveillance for late effects. Beyond 5 years, care for cancer survivors mainly involves primary care and management of any comorbid conditions, as well as surveillance for late effects and recurrences, and awareness of new research.

A patient consultation early in the process after diagnosis is the start of a continuum of care, Dr. Ganz said. A patient consultation should address symptoms related to initial treatments, such as neuropathy, pain, fatigue, and insomnia, as well as the psychological symptoms of anxiety and depression. An early consultation also should evaluate adherence to endocrine therapy and management of symptoms, if needed, with the larger goal of preparing patients for recovery and the transition to survivorship, and what to expect for long-term follow-up.
 

Delivering the three P’s

The “Three P’s” of survivor care for breast cancer patients are palliation, prevention, and promotion of health, according to Dr. Ganz .

The first “P,” for palliative, is a key part of survivorship care, said Dr. Ganz. Palliative care is defined as care that focuses on reducing symptom severity and improving quality of life. The biological effects of cancer treatment can be associated with physical effects, such as functional limitations and frailty, and behavioral/cognitive effects such as depression, fatigue, and cognitive deficits, she said. To manage these effects and provide palliative care, consultation is needed with specialists in relevant areas including mental health, pain management, physical medicine/rehabilitation, endocrinology, cardiology, and neurology.

The second “P,” which is for prevention in survivorship care, refers to ongoing follow-up screening to identify any potentially serious late-onset complications such as osteoporosis or cardiac disease so they can be addressed, said Dr. Ganz. Other considerations include chemoprevention if available and genetic counseling for patients with hereditary cancers. Prevention also includes counseling patients about lifestyle modifications to help prevent additional cancer.

The goal of the third “P,” which is for health promotion, is to promote risk reduction for the health problems associated with accelerated aging that may arise in cancer survivors, said Dr. Ganz.

Health promotion strategies include maintaining a healthy weight, increasing physical activity, and avoiding harmful exposures, she said. Healthy lifestyle interventions can also reduce the risk of other chronic diseases such as diabetes and heart disease.

To that end, Dr. Ganz outlined several behavioral interventions that may mitigate the effects of cancer treatment on the accelerated aging process, including stress reduction in the form of meditation or yoga, cognitive behavioral therapy, improving sleep, increasing physical activity, reducing obesity, and decreasing tobacco and alcohol use. These interventions may help reduce inflammation and promote tissue repair and healing.

For cancer survivors, the life span may be longer than the health span, and these patients may benefit from an integrated model of care, with systematic screening and consolidated appointments, rather than a fragmented model in which departments and referrals are siloed, which may result in conflicting advice or redundancy, said Dr. Ganz.

Looking ahead, more research is needed to explore models of care delivery, as requirements for survivor care will vary among patients and care settings, Dr. Ganz said.

However, regardless of setting, treatment plans and shared decision-making can help reduce potential long-term or late-emerging effects, she said. Developing a survivorship care plan can help patients learn how to enhance their recovery.

During a question and answer session, Dr. Ganz was asked about whether hormone therapy could be used for patients with hormone negative breast cancer. “I think vaginal estrogen can be used if someone is on tamoxifen,” she said. However, “we need to be cautious” in case there are remaining estrogen positive cells, in order to avoid potential metastases, and use of hormone therapy in breast cancer survivors is an individualized decision based in part on quality of life.
 

Engaging a patient’s partner early can be helpful

If possible, engage the patient’s partner in survivorship discussions, said Luzia Travado, PhD, head of psycho-oncology at the Champalimaud Foundation, Lisbon, who presented on the topic of sexuality and commented on survivorship during the discussion. For those women with partners, engaging the partner early in treatment often means they are more likely to play a larger role in the post treatment and long term by providing stability and emotional support.

“Make sure partners are engaged and understand that they have a role, and that this role is valued,” she said. Unfortunately, there are a lot of divorced women with breast cancer, as the disease can take a toll on relationships. However, remember “sexuality is not just sex; it is caring, loving, and intimacy.”

“To end on a positive note, it is important to empower patients, and to give them self-management skills so they can make things even better in their survivorship,” said Dr. Ganz. In spite of discussing difficulties and challenges, one of the goals of the session was to offer potential solutions and answers.

Dr. Ganz disclosed serving as editor of the cancer survivorship section on Up-to-Date, and serving as a consultant for Blue Note Therapeutics, GRAIL, InformedDNA, and Roche-Genentech. Dr. Travado had no relevant financial conflicts to disclose.

“What am I allowed to do?” radiation oncologist Vivek Kavadi, MD, asked the business manager at Texas Oncology in Sugar Land, Tex.

Dr. Kavadi wanted to give his patient with early-stage breast cancer a standard radiation treatment – hypofractionated 3D conformal radiation therapy – following her lumpectomy.

But his hands were tied.

Dr. Kavadi had submitted a prior authorization request, but the patient’s health insurance was dragging its feet. And without prior authorization, Dr. Kavadi couldn’t schedule his patient’s first treatment.

“I chose the most cost-effective, standard treatment, but nothing could begin without the insurance company’s permission,” he said.

One of the most challenging aspects of the delay was explaining to his patient why he couldn’t schedule her treatment. “We would love to start, but your insurance company has not given us approval. The best I can do is give you a tentative appointment,” he recalled telling her.

After a few days with no word, calls to the insurance company began. “My patient called, I called, my office called,” Dr. Kavadi said. “It was a week or more of aggravation, stress, and time wasted for my patient and my team.”

This type of delay has become increasingly common in radiation oncology. One recent analysis estimated that 97% of radiation oncology services now require prior authorization under Medicare Advantage. And another analysis found that almost all radiation oncologists said prior authorization delays life-saving care for their patients.

The rise of prior authorization requirements may boil down to the fact that prior authorization is no longer reserved for uncertain or high-cost care. Terrence Cunningham, director of administrative simplification policy for the American Hospital Association, told this news organization last year that “prior authorization used to be applied only to new, costly, or high-risk services,” but now “many insurers require authorizations for even routine care, which is inappropriate.”

The growth of prior authorization requirements has forced many doctors, nurses, and pharmacists to dedicate part of their workday to handling requests and appealing denials and has forced many practices to hire staff exclusively for prior authorizations.

This additional work is costly.

One recent study found that the radiation oncology department of Vanderbilt University, Nashville, Tenn., spent nearly $500,000 annually in employee time to obtain prior authorization for radiation therapy treatments. Extrapolated nationally, the researchers estimated that physicians’ annual compensation for prior authorization duties came to an estimated $46 million. Overall, 86% of these costs were for treatments that were ultimately approved, the majority on initial request and some on appeal.

Dr. Kavadi has five full-time employees dedicated to managing prior authorization requests and challenges.

And after a week of delays and hours on the phone with the insurer, his patient’s radiation treatment was ultimately approved.

Dr. Kavadi wondered why something so simple needed to be so onerous.

Stretching out an approval for a standard radiation treatment “feels like a means of intentionally delaying care,” Dr. Kavadi said. “This is an example of a process that has run so far amok. It’s just a burden across the board.”

And even with his 30 years of experience, “I still have to ask my business supervisor what I am allowed to do,” he said. “I can’t just proceed with what’s best for my patient, what the patient has consented to, and what also happens to be the least expensive option.”

A version of this article first appeared on Medscape.com.

“What am I allowed to do?” radiation oncologist Vivek Kavadi, MD, asked the business manager at Texas Oncology in Sugar Land, Tex.

Dr. Kavadi wanted to give his patient with early-stage breast cancer a standard radiation treatment – hypofractionated 3D conformal radiation therapy – following her lumpectomy.

But his hands were tied.

Dr. Kavadi had submitted a prior authorization request, but the patient’s health insurance was dragging its feet. And without prior authorization, Dr. Kavadi couldn’t schedule his patient’s first treatment.

“I chose the most cost-effective, standard treatment, but nothing could begin without the insurance company’s permission,” he said.

One of the most challenging aspects of the delay was explaining to his patient why he couldn’t schedule her treatment. “We would love to start, but your insurance company has not given us approval. The best I can do is give you a tentative appointment,” he recalled telling her.

After a few days with no word, calls to the insurance company began. “My patient called, I called, my office called,” Dr. Kavadi said. “It was a week or more of aggravation, stress, and time wasted for my patient and my team.”

This type of delay has become increasingly common in radiation oncology. One recent analysis estimated that 97% of radiation oncology services now require prior authorization under Medicare Advantage. And another analysis found that almost all radiation oncologists said prior authorization delays life-saving care for their patients.

The rise of prior authorization requirements may boil down to the fact that prior authorization is no longer reserved for uncertain or high-cost care. Terrence Cunningham, director of administrative simplification policy for the American Hospital Association, told this news organization last year that “prior authorization used to be applied only to new, costly, or high-risk services,” but now “many insurers require authorizations for even routine care, which is inappropriate.”

The growth of prior authorization requirements has forced many doctors, nurses, and pharmacists to dedicate part of their workday to handling requests and appealing denials and has forced many practices to hire staff exclusively for prior authorizations.

This additional work is costly.

One recent study found that the radiation oncology department of Vanderbilt University, Nashville, Tenn., spent nearly $500,000 annually in employee time to obtain prior authorization for radiation therapy treatments. Extrapolated nationally, the researchers estimated that physicians’ annual compensation for prior authorization duties came to an estimated $46 million. Overall, 86% of these costs were for treatments that were ultimately approved, the majority on initial request and some on appeal.

Dr. Kavadi has five full-time employees dedicated to managing prior authorization requests and challenges.

And after a week of delays and hours on the phone with the insurer, his patient’s radiation treatment was ultimately approved.

Dr. Kavadi wondered why something so simple needed to be so onerous.

Stretching out an approval for a standard radiation treatment “feels like a means of intentionally delaying care,” Dr. Kavadi said. “This is an example of a process that has run so far amok. It’s just a burden across the board.”

And even with his 30 years of experience, “I still have to ask my business supervisor what I am allowed to do,” he said. “I can’t just proceed with what’s best for my patient, what the patient has consented to, and what also happens to be the least expensive option.”

A version of this article first appeared on Medscape.com.

“What am I allowed to do?” radiation oncologist Vivek Kavadi, MD, asked the business manager at Texas Oncology in Sugar Land, Tex.

Dr. Kavadi wanted to give his patient with early-stage breast cancer a standard radiation treatment – hypofractionated 3D conformal radiation therapy – following her lumpectomy.

But his hands were tied.

Dr. Kavadi had submitted a prior authorization request, but the patient’s health insurance was dragging its feet. And without prior authorization, Dr. Kavadi couldn’t schedule his patient’s first treatment.

“I chose the most cost-effective, standard treatment, but nothing could begin without the insurance company’s permission,” he said.

One of the most challenging aspects of the delay was explaining to his patient why he couldn’t schedule her treatment. “We would love to start, but your insurance company has not given us approval. The best I can do is give you a tentative appointment,” he recalled telling her.

After a few days with no word, calls to the insurance company began. “My patient called, I called, my office called,” Dr. Kavadi said. “It was a week or more of aggravation, stress, and time wasted for my patient and my team.”

This type of delay has become increasingly common in radiation oncology. One recent analysis estimated that 97% of radiation oncology services now require prior authorization under Medicare Advantage. And another analysis found that almost all radiation oncologists said prior authorization delays life-saving care for their patients.

The rise of prior authorization requirements may boil down to the fact that prior authorization is no longer reserved for uncertain or high-cost care. Terrence Cunningham, director of administrative simplification policy for the American Hospital Association, told this news organization last year that “prior authorization used to be applied only to new, costly, or high-risk services,” but now “many insurers require authorizations for even routine care, which is inappropriate.”

The growth of prior authorization requirements has forced many doctors, nurses, and pharmacists to dedicate part of their workday to handling requests and appealing denials and has forced many practices to hire staff exclusively for prior authorizations.

This additional work is costly.

One recent study found that the radiation oncology department of Vanderbilt University, Nashville, Tenn., spent nearly $500,000 annually in employee time to obtain prior authorization for radiation therapy treatments. Extrapolated nationally, the researchers estimated that physicians’ annual compensation for prior authorization duties came to an estimated $46 million. Overall, 86% of these costs were for treatments that were ultimately approved, the majority on initial request and some on appeal.

Dr. Kavadi has five full-time employees dedicated to managing prior authorization requests and challenges.

And after a week of delays and hours on the phone with the insurer, his patient’s radiation treatment was ultimately approved.

Dr. Kavadi wondered why something so simple needed to be so onerous.

Stretching out an approval for a standard radiation treatment “feels like a means of intentionally delaying care,” Dr. Kavadi said. “This is an example of a process that has run so far amok. It’s just a burden across the board.”

And even with his 30 years of experience, “I still have to ask my business supervisor what I am allowed to do,” he said. “I can’t just proceed with what’s best for my patient, what the patient has consented to, and what also happens to be the least expensive option.”

A version of this article first appeared on Medscape.com.

Inpatient dermatology consultation services are becoming increasingly prevalent in hospital institutions.^1-3 Although often underutilized as a consulting service, dermatology-related admissions cost hundreds of millions of dollars for the health care system.^1,2 Misdiagnosis, prolonged hospital stays, and incorrect treatment are common results of lack of involvement by a skin expert.^1-3 The importance of consultative inpatient dermatology cannot be understated. Accreditation Council for Graduate Medical Education requirements for proficiency in dermatology residency include exposure to inpatient dermatology, and it is our duty as residents to aid our colleagues in the management and treatment of cutaneous disease.

Although exposure to inpatient dermatology varies across residency programs, nearly every dermatology resident is bound to perform a consultation and be involved in the care of a hospitalized patient. At our program at the University of Utah (Salt Lake City), we have robust inpatient exposure, and after numerous hours spent on the forefront of inpatient dermatology, I have accrued a list of specific tips and techniques that have aided me as a resident clinician.

Pre-Rounding More Thoroughly

When I started as a postgraduate year 2 (PGY-2) on the inpatient dermatology rotation, I found myself perplexed. I had learned how to round in internal medicine but was unaccustomed to the nuances of specialty rounds. My list included calciphylaxis, small vessel vasculitis, cellulitis, stasis dermatitis, toxic epidermal necrolysis, and atypical mycobacterial infection. The first few days of service were undeniably difficult due to the daily consultations, complexity of admitted patients, and need for efficiency. I sometimes overlooked important laboratory test results, medication changes, and interdisciplinary discussions that prolonged rounding. As dermatologists, we are responsible for the largest organ of the body, and it is important to approach patients in a comprehensive manner. Pre-rounding should include reviewing interdisciplinary notes, laboratory values/results, and medications, and performing a focused skin examination with a review of systems during the encounter. Importantly, most electronic medical record systems offer an automated rounding sheet. In Epic (Epic Systems Corporation), I would use the bone marrow transplant rounding sheet, which includes laboratory test results, vitals, and medications. After printing out the rounding sheet, I would note important updates for each patient. Although pre-rounding and chart review requires time and effort, it aided me in providing elevated patient care and becoming more efficient during rounds. Over time I have come to strongly appreciate the term dermatology hospitalist. Cutaneous manifestations of systemic disease require thoughtful consideration and workup.

New Patient Consultations: Must-Ask Questions

Holding the university inpatient pager can be stressful. At the University of Utah, we often carry 5 to 10 patients on our list and receive 3 to 4 new consultations a day, sometimes right before 5 pm. When receiving new consultations, it is important to obtain the whole story from the requesting provider. Some of the questions may seem obvious, but I am frequently reminded of the inefficiencies I encountered as a first-year dermatology resident. Remember to always be kind to your consulting colleagues. Dermatology is a difficult discipline, and describing rashes is no easy task. Here is my systematic approach to questions that should be asked of the requesting provider during each new patient consultation:

• What is the patient’s name, room number, and medical record number?
• Is this patient getting admitted or admitted currently?
• Is the rash the reason for admission? (This can greatly help with triaging the urgency of evaluation.)
• Is the rash painful?
• Is this patient ill?
• How would you describe the rash?

When evaluating new patients, it is crucial to remember the morphology camps. Formulating a differential diagnosis on a complex patient can be difficult; however, remembering the morphology camps of acneiform, dermal, eczematous, erythematous, subcutaneous, vasculitic, vasculopathic, and vesiculobullous lesions can be extremely helpful. Additionally, it is crucial to perform a thorough and complete skin examination on every patient. When emphasizing the importance of this, I often am reminded of a humbling moment early in my training. Our team was consulted on a patient with cellulitis and stasis dermatitis. It was a busy day, and my examination was quick and focused on the lower and upper extremities, chest, and back. The patient improved from a cutaneous standpoint and was discharged. At follow-up the next week, one of my attending providers biopsied an atypical macule on the retroauricular region, which was found to be consistent with a stage 1A melanoma. Even on the longest and most tiring hospital days, it is important to perform a full-body skin examination on each patient. You may end up saving a life.

An Organized Toolbox: What to Carry

Similar to our ophthalmology colleagues who are seen carrying around a suitcase in the hospital, I highly recommend some form of a toolbox or bag for performing inpatient biopsies (Table). Carrying around an organized bag, albeit bulky and unfashionable, has saved me numerous trips back to clinic for unexpected complications including fixing leaky vessels, closing stubborn ulcers, and coordinating sedated biopsies in the operating room.

Final Thoughts

As I near the completion of my residency journey, I hope these tips will aid budding and current dermatology residents at excelling as dermatology hospitalists during inpatient rotations. Dermatologists can make a profound impact on a variety of patients, especially when treating hospitalized patients on the clinical forefront. Our role extends beyond the skin, as cutaneous manifestations of internal disease are not uncommon.

Inpatient dermatology consultation services are becoming increasingly prevalent in hospital institutions.^1-3 Although often underutilized as a consulting service, dermatology-related admissions cost hundreds of millions of dollars for the health care system.^1,2 Misdiagnosis, prolonged hospital stays, and incorrect treatment are common results of lack of involvement by a skin expert.^1-3 The importance of consultative inpatient dermatology cannot be understated. Accreditation Council for Graduate Medical Education requirements for proficiency in dermatology residency include exposure to inpatient dermatology, and it is our duty as residents to aid our colleagues in the management and treatment of cutaneous disease.

Although exposure to inpatient dermatology varies across residency programs, nearly every dermatology resident is bound to perform a consultation and be involved in the care of a hospitalized patient. At our program at the University of Utah (Salt Lake City), we have robust inpatient exposure, and after numerous hours spent on the forefront of inpatient dermatology, I have accrued a list of specific tips and techniques that have aided me as a resident clinician.

Pre-Rounding More Thoroughly

When I started as a postgraduate year 2 (PGY-2) on the inpatient dermatology rotation, I found myself perplexed. I had learned how to round in internal medicine but was unaccustomed to the nuances of specialty rounds. My list included calciphylaxis, small vessel vasculitis, cellulitis, stasis dermatitis, toxic epidermal necrolysis, and atypical mycobacterial infection. The first few days of service were undeniably difficult due to the daily consultations, complexity of admitted patients, and need for efficiency. I sometimes overlooked important laboratory test results, medication changes, and interdisciplinary discussions that prolonged rounding. As dermatologists, we are responsible for the largest organ of the body, and it is important to approach patients in a comprehensive manner. Pre-rounding should include reviewing interdisciplinary notes, laboratory values/results, and medications, and performing a focused skin examination with a review of systems during the encounter. Importantly, most electronic medical record systems offer an automated rounding sheet. In Epic (Epic Systems Corporation), I would use the bone marrow transplant rounding sheet, which includes laboratory test results, vitals, and medications. After printing out the rounding sheet, I would note important updates for each patient. Although pre-rounding and chart review requires time and effort, it aided me in providing elevated patient care and becoming more efficient during rounds. Over time I have come to strongly appreciate the term dermatology hospitalist. Cutaneous manifestations of systemic disease require thoughtful consideration and workup.

New Patient Consultations: Must-Ask Questions

Holding the university inpatient pager can be stressful. At the University of Utah, we often carry 5 to 10 patients on our list and receive 3 to 4 new consultations a day, sometimes right before 5 pm. When receiving new consultations, it is important to obtain the whole story from the requesting provider. Some of the questions may seem obvious, but I am frequently reminded of the inefficiencies I encountered as a first-year dermatology resident. Remember to always be kind to your consulting colleagues. Dermatology is a difficult discipline, and describing rashes is no easy task. Here is my systematic approach to questions that should be asked of the requesting provider during each new patient consultation:

• What is the patient’s name, room number, and medical record number?
• Is this patient getting admitted or admitted currently?
• Is the rash the reason for admission? (This can greatly help with triaging the urgency of evaluation.)
• Is the rash painful?
• Is this patient ill?
• How would you describe the rash?

When evaluating new patients, it is crucial to remember the morphology camps. Formulating a differential diagnosis on a complex patient can be difficult; however, remembering the morphology camps of acneiform, dermal, eczematous, erythematous, subcutaneous, vasculitic, vasculopathic, and vesiculobullous lesions can be extremely helpful. Additionally, it is crucial to perform a thorough and complete skin examination on every patient. When emphasizing the importance of this, I often am reminded of a humbling moment early in my training. Our team was consulted on a patient with cellulitis and stasis dermatitis. It was a busy day, and my examination was quick and focused on the lower and upper extremities, chest, and back. The patient improved from a cutaneous standpoint and was discharged. At follow-up the next week, one of my attending providers biopsied an atypical macule on the retroauricular region, which was found to be consistent with a stage 1A melanoma. Even on the longest and most tiring hospital days, it is important to perform a full-body skin examination on each patient. You may end up saving a life.

An Organized Toolbox: What to Carry

Similar to our ophthalmology colleagues who are seen carrying around a suitcase in the hospital, I highly recommend some form of a toolbox or bag for performing inpatient biopsies (Table). Carrying around an organized bag, albeit bulky and unfashionable, has saved me numerous trips back to clinic for unexpected complications including fixing leaky vessels, closing stubborn ulcers, and coordinating sedated biopsies in the operating room.

Final Thoughts

As I near the completion of my residency journey, I hope these tips will aid budding and current dermatology residents at excelling as dermatology hospitalists during inpatient rotations. Dermatologists can make a profound impact on a variety of patients, especially when treating hospitalized patients on the clinical forefront. Our role extends beyond the skin, as cutaneous manifestations of internal disease are not uncommon.

Inpatient dermatology consultation services are becoming increasingly prevalent in hospital institutions.^1-3 Although often underutilized as a consulting service, dermatology-related admissions cost hundreds of millions of dollars for the health care system.^1,2 Misdiagnosis, prolonged hospital stays, and incorrect treatment are common results of lack of involvement by a skin expert.^1-3 The importance of consultative inpatient dermatology cannot be understated. Accreditation Council for Graduate Medical Education requirements for proficiency in dermatology residency include exposure to inpatient dermatology, and it is our duty as residents to aid our colleagues in the management and treatment of cutaneous disease.

Although exposure to inpatient dermatology varies across residency programs, nearly every dermatology resident is bound to perform a consultation and be involved in the care of a hospitalized patient. At our program at the University of Utah (Salt Lake City), we have robust inpatient exposure, and after numerous hours spent on the forefront of inpatient dermatology, I have accrued a list of specific tips and techniques that have aided me as a resident clinician.

Pre-Rounding More Thoroughly

When I started as a postgraduate year 2 (PGY-2) on the inpatient dermatology rotation, I found myself perplexed. I had learned how to round in internal medicine but was unaccustomed to the nuances of specialty rounds. My list included calciphylaxis, small vessel vasculitis, cellulitis, stasis dermatitis, toxic epidermal necrolysis, and atypical mycobacterial infection. The first few days of service were undeniably difficult due to the daily consultations, complexity of admitted patients, and need for efficiency. I sometimes overlooked important laboratory test results, medication changes, and interdisciplinary discussions that prolonged rounding. As dermatologists, we are responsible for the largest organ of the body, and it is important to approach patients in a comprehensive manner. Pre-rounding should include reviewing interdisciplinary notes, laboratory values/results, and medications, and performing a focused skin examination with a review of systems during the encounter. Importantly, most electronic medical record systems offer an automated rounding sheet. In Epic (Epic Systems Corporation), I would use the bone marrow transplant rounding sheet, which includes laboratory test results, vitals, and medications. After printing out the rounding sheet, I would note important updates for each patient. Although pre-rounding and chart review requires time and effort, it aided me in providing elevated patient care and becoming more efficient during rounds. Over time I have come to strongly appreciate the term dermatology hospitalist. Cutaneous manifestations of systemic disease require thoughtful consideration and workup.

New Patient Consultations: Must-Ask Questions

Holding the university inpatient pager can be stressful. At the University of Utah, we often carry 5 to 10 patients on our list and receive 3 to 4 new consultations a day, sometimes right before 5 pm. When receiving new consultations, it is important to obtain the whole story from the requesting provider. Some of the questions may seem obvious, but I am frequently reminded of the inefficiencies I encountered as a first-year dermatology resident. Remember to always be kind to your consulting colleagues. Dermatology is a difficult discipline, and describing rashes is no easy task. Here is my systematic approach to questions that should be asked of the requesting provider during each new patient consultation:

• What is the patient’s name, room number, and medical record number?
• Is this patient getting admitted or admitted currently?
• Is the rash the reason for admission? (This can greatly help with triaging the urgency of evaluation.)
• Is the rash painful?
• Is this patient ill?
• How would you describe the rash?

When evaluating new patients, it is crucial to remember the morphology camps. Formulating a differential diagnosis on a complex patient can be difficult; however, remembering the morphology camps of acneiform, dermal, eczematous, erythematous, subcutaneous, vasculitic, vasculopathic, and vesiculobullous lesions can be extremely helpful. Additionally, it is crucial to perform a thorough and complete skin examination on every patient. When emphasizing the importance of this, I often am reminded of a humbling moment early in my training. Our team was consulted on a patient with cellulitis and stasis dermatitis. It was a busy day, and my examination was quick and focused on the lower and upper extremities, chest, and back. The patient improved from a cutaneous standpoint and was discharged. At follow-up the next week, one of my attending providers biopsied an atypical macule on the retroauricular region, which was found to be consistent with a stage 1A melanoma. Even on the longest and most tiring hospital days, it is important to perform a full-body skin examination on each patient. You may end up saving a life.

An Organized Toolbox: What to Carry

Similar to our ophthalmology colleagues who are seen carrying around a suitcase in the hospital, I highly recommend some form of a toolbox or bag for performing inpatient biopsies (Table). Carrying around an organized bag, albeit bulky and unfashionable, has saved me numerous trips back to clinic for unexpected complications including fixing leaky vessels, closing stubborn ulcers, and coordinating sedated biopsies in the operating room.

Final Thoughts

As I near the completion of my residency journey, I hope these tips will aid budding and current dermatology residents at excelling as dermatology hospitalists during inpatient rotations. Dermatologists can make a profound impact on a variety of patients, especially when treating hospitalized patients on the clinical forefront. Our role extends beyond the skin, as cutaneous manifestations of internal disease are not uncommon.

Leaders of medical student groups and legislators in a few states are trying to convince medical schools to end a century-old practice of legacy admissions, which they say offer preferential treatment to applicants based on their association with donors or alumni.

While an estimated 25% of public colleges and universities still use legacy admissions, a growing list of top medical schools have moved away from the practice over the last decade, including Johns Hopkins University, Baltimore, and Tufts University, Medford, Mass.

Legacy admissions contradict schools’ more inclusive policies, Senila Yasmin, MPH, a second-year medical student at Tufts University, said in an interview. While Tufts maintains legacy admissions for its undergraduate applicants, the medical school stopped the practice in 2021, said Ms. Yasmin, a member of a student group that lobbied against the school’s legacy preferences.

Describing herself as a low-income, first-generation Muslim-Pakistani American, Ms. Yasmin wants to use her experience at Tufts to improve accessibility for students like herself.

As a member of the American Medical Association (AMA) Medical Student Section, she coauthored a resolution stating that legacy admissions go against the AMA’s strategic plan to advance racial justice and health equity. The Student Section passed the resolution in November, and in June, the AMA House of Delegates will vote on whether to adopt the policy. 

Along with a Supreme Court decision that could strike down race-conscious college admissions, an AMA policy could convince medical schools to rethink legacy admissions and how to maintain diverse student bodies. In June, the court is expected to issue a decision in the Students for Fair Admissions lawsuit against Harvard University, Cambridge, Mass., and the University of North Carolina, Chapel Hill, which alleges that considering race in holistic admissions constitutes racial discrimination and violates the Equal Protection Clause.

Opponents of legacy admissions, like Ms. Yasmin, say it penalizes students from racial minorities and lower socioeconomic backgrounds, hampering a fair and equitable admissions process that attracts diverse medical school admissions.
 

Diversity of medical applicants

Diversity in medical schools  continued to increase last year with more Black, Hispanic, and female students applying and enrolling, according to a recent report by the Association of American Medical Colleges (AAMC). However, universities often include nonacademic criteria in their admission assessments to improve educational access for underrepresented minorities.

Medical schools carefully consider each applicant’s background “to yield a diverse class of students,” Geoffrey Young, PhD, AAMC’s senior director of transforming the health care workforce, told this news organization.

Some schools, such as Morehouse School of Medicine, Atlanta, the University of Virginia School of Medicine, Charlottesville, and the University of Arizona College of Medicine, Tucson, perform a thorough review of candidates while offering admissions practices designed specifically for legacy applicants. The schools assert that legacy designation doesn’t factor into the student’s likelihood of acceptance.

The arrangement may show that schools want to commit to equity and fairness but have trouble moving away from entrenched traditions, two professors from Penn State College of Medicine, Hershey, Pa., who sit on separate medical admissions subcommittees, wrote last year in Bioethics Today.
 

Legislation may hasten legacies’ end

In December, Ms. Yasmin and a group of Massachusetts Medical Society student-members presented another resolution to the state medical society, which adopted it.

The society’s new policy opposes the use of legacy status in medical school admissions and supports mechanisms to eliminate its inclusion from the application process, Theodore Calianos II, MD, FACS, president of the Massachusetts Medical Society, said in an interview.

“Legacy preferences limit racial and socioeconomic diversity on campuses, so we asked, ‘What can we do so that everyone has equal access to medical education?’ It is exciting to see the students and young physicians – the future of medicine – become involved in policymaking.”

Proposed laws may also hasten the end of legacy admissions. Last year, the U.S. Senate began considering a bill prohibiting colleges receiving federal financial aid from giving preferential treatment to students based on their relations to donors or alumni. However, the bill allows the Department of Education to make exceptions for institutions serving historically underrepresented groups.

The New York State Senate and the New York State Assembly also are reviewing bills that ban legacy and early admissions policies at public and private universities. Connecticut announced similar legislation last year. Massachusetts legislators are considering two bills: one that would ban the practice at the state’s public universities and another that would require all schools using legacy status to pay a “public service fee” equal to a percentage of its endowment. Colleges with endowment assets exceeding $2 billion must pay at least $2 million, according to the bill’s text.

At schools like Harvard,  whose endowment surpasses $50 billion, the option to pay the penalty will make the law moot, Michael Walls, DO, MPH, president of the American Medical Student Association (AMSA), said in an interview. “Smaller schools wouldn’t be able to afford the fine and are less likely to be doing [legacy admissions] anyway,” he said. “The schools that want to continue doing it could just pay the fine.”

Dr. Walls said AMSA supports race-conscious admissions processes and anything that increases fairness for medical school applicants. “Whatever [fair] means is up for interpretation, but it would be great to eliminate legacy admissions,” he said.   
 

A version of this article originally appeared on Medscape.com.

Leaders of medical student groups and legislators in a few states are trying to convince medical schools to end a century-old practice of legacy admissions, which they say offer preferential treatment to applicants based on their association with donors or alumni.

While an estimated 25% of public colleges and universities still use legacy admissions, a growing list of top medical schools have moved away from the practice over the last decade, including Johns Hopkins University, Baltimore, and Tufts University, Medford, Mass.

Legacy admissions contradict schools’ more inclusive policies, Senila Yasmin, MPH, a second-year medical student at Tufts University, said in an interview. While Tufts maintains legacy admissions for its undergraduate applicants, the medical school stopped the practice in 2021, said Ms. Yasmin, a member of a student group that lobbied against the school’s legacy preferences.

Describing herself as a low-income, first-generation Muslim-Pakistani American, Ms. Yasmin wants to use her experience at Tufts to improve accessibility for students like herself.

As a member of the American Medical Association (AMA) Medical Student Section, she coauthored a resolution stating that legacy admissions go against the AMA’s strategic plan to advance racial justice and health equity. The Student Section passed the resolution in November, and in June, the AMA House of Delegates will vote on whether to adopt the policy. 

Along with a Supreme Court decision that could strike down race-conscious college admissions, an AMA policy could convince medical schools to rethink legacy admissions and how to maintain diverse student bodies. In June, the court is expected to issue a decision in the Students for Fair Admissions lawsuit against Harvard University, Cambridge, Mass., and the University of North Carolina, Chapel Hill, which alleges that considering race in holistic admissions constitutes racial discrimination and violates the Equal Protection Clause.

Opponents of legacy admissions, like Ms. Yasmin, say it penalizes students from racial minorities and lower socioeconomic backgrounds, hampering a fair and equitable admissions process that attracts diverse medical school admissions.
 

Diversity of medical applicants

Diversity in medical schools  continued to increase last year with more Black, Hispanic, and female students applying and enrolling, according to a recent report by the Association of American Medical Colleges (AAMC). However, universities often include nonacademic criteria in their admission assessments to improve educational access for underrepresented minorities.

Medical schools carefully consider each applicant’s background “to yield a diverse class of students,” Geoffrey Young, PhD, AAMC’s senior director of transforming the health care workforce, told this news organization.

Some schools, such as Morehouse School of Medicine, Atlanta, the University of Virginia School of Medicine, Charlottesville, and the University of Arizona College of Medicine, Tucson, perform a thorough review of candidates while offering admissions practices designed specifically for legacy applicants. The schools assert that legacy designation doesn’t factor into the student’s likelihood of acceptance.

The arrangement may show that schools want to commit to equity and fairness but have trouble moving away from entrenched traditions, two professors from Penn State College of Medicine, Hershey, Pa., who sit on separate medical admissions subcommittees, wrote last year in Bioethics Today.
 

Legislation may hasten legacies’ end

In December, Ms. Yasmin and a group of Massachusetts Medical Society student-members presented another resolution to the state medical society, which adopted it.

The society’s new policy opposes the use of legacy status in medical school admissions and supports mechanisms to eliminate its inclusion from the application process, Theodore Calianos II, MD, FACS, president of the Massachusetts Medical Society, said in an interview.

“Legacy preferences limit racial and socioeconomic diversity on campuses, so we asked, ‘What can we do so that everyone has equal access to medical education?’ It is exciting to see the students and young physicians – the future of medicine – become involved in policymaking.”

Proposed laws may also hasten the end of legacy admissions. Last year, the U.S. Senate began considering a bill prohibiting colleges receiving federal financial aid from giving preferential treatment to students based on their relations to donors or alumni. However, the bill allows the Department of Education to make exceptions for institutions serving historically underrepresented groups.

The New York State Senate and the New York State Assembly also are reviewing bills that ban legacy and early admissions policies at public and private universities. Connecticut announced similar legislation last year. Massachusetts legislators are considering two bills: one that would ban the practice at the state’s public universities and another that would require all schools using legacy status to pay a “public service fee” equal to a percentage of its endowment. Colleges with endowment assets exceeding $2 billion must pay at least $2 million, according to the bill’s text.

At schools like Harvard,  whose endowment surpasses $50 billion, the option to pay the penalty will make the law moot, Michael Walls, DO, MPH, president of the American Medical Student Association (AMSA), said in an interview. “Smaller schools wouldn’t be able to afford the fine and are less likely to be doing [legacy admissions] anyway,” he said. “The schools that want to continue doing it could just pay the fine.”

Dr. Walls said AMSA supports race-conscious admissions processes and anything that increases fairness for medical school applicants. “Whatever [fair] means is up for interpretation, but it would be great to eliminate legacy admissions,” he said.   
 

A version of this article originally appeared on Medscape.com.

Leaders of medical student groups and legislators in a few states are trying to convince medical schools to end a century-old practice of legacy admissions, which they say offer preferential treatment to applicants based on their association with donors or alumni.

While an estimated 25% of public colleges and universities still use legacy admissions, a growing list of top medical schools have moved away from the practice over the last decade, including Johns Hopkins University, Baltimore, and Tufts University, Medford, Mass.

Legacy admissions contradict schools’ more inclusive policies, Senila Yasmin, MPH, a second-year medical student at Tufts University, said in an interview. While Tufts maintains legacy admissions for its undergraduate applicants, the medical school stopped the practice in 2021, said Ms. Yasmin, a member of a student group that lobbied against the school’s legacy preferences.

Describing herself as a low-income, first-generation Muslim-Pakistani American, Ms. Yasmin wants to use her experience at Tufts to improve accessibility for students like herself.

As a member of the American Medical Association (AMA) Medical Student Section, she coauthored a resolution stating that legacy admissions go against the AMA’s strategic plan to advance racial justice and health equity. The Student Section passed the resolution in November, and in June, the AMA House of Delegates will vote on whether to adopt the policy. 

Along with a Supreme Court decision that could strike down race-conscious college admissions, an AMA policy could convince medical schools to rethink legacy admissions and how to maintain diverse student bodies. In June, the court is expected to issue a decision in the Students for Fair Admissions lawsuit against Harvard University, Cambridge, Mass., and the University of North Carolina, Chapel Hill, which alleges that considering race in holistic admissions constitutes racial discrimination and violates the Equal Protection Clause.

Opponents of legacy admissions, like Ms. Yasmin, say it penalizes students from racial minorities and lower socioeconomic backgrounds, hampering a fair and equitable admissions process that attracts diverse medical school admissions.
 

Diversity of medical applicants

Diversity in medical schools  continued to increase last year with more Black, Hispanic, and female students applying and enrolling, according to a recent report by the Association of American Medical Colleges (AAMC). However, universities often include nonacademic criteria in their admission assessments to improve educational access for underrepresented minorities.

Medical schools carefully consider each applicant’s background “to yield a diverse class of students,” Geoffrey Young, PhD, AAMC’s senior director of transforming the health care workforce, told this news organization.

Some schools, such as Morehouse School of Medicine, Atlanta, the University of Virginia School of Medicine, Charlottesville, and the University of Arizona College of Medicine, Tucson, perform a thorough review of candidates while offering admissions practices designed specifically for legacy applicants. The schools assert that legacy designation doesn’t factor into the student’s likelihood of acceptance.

The arrangement may show that schools want to commit to equity and fairness but have trouble moving away from entrenched traditions, two professors from Penn State College of Medicine, Hershey, Pa., who sit on separate medical admissions subcommittees, wrote last year in Bioethics Today.
 

Legislation may hasten legacies’ end

In December, Ms. Yasmin and a group of Massachusetts Medical Society student-members presented another resolution to the state medical society, which adopted it.

The society’s new policy opposes the use of legacy status in medical school admissions and supports mechanisms to eliminate its inclusion from the application process, Theodore Calianos II, MD, FACS, president of the Massachusetts Medical Society, said in an interview.

“Legacy preferences limit racial and socioeconomic diversity on campuses, so we asked, ‘What can we do so that everyone has equal access to medical education?’ It is exciting to see the students and young physicians – the future of medicine – become involved in policymaking.”

Proposed laws may also hasten the end of legacy admissions. Last year, the U.S. Senate began considering a bill prohibiting colleges receiving federal financial aid from giving preferential treatment to students based on their relations to donors or alumni. However, the bill allows the Department of Education to make exceptions for institutions serving historically underrepresented groups.

The New York State Senate and the New York State Assembly also are reviewing bills that ban legacy and early admissions policies at public and private universities. Connecticut announced similar legislation last year. Massachusetts legislators are considering two bills: one that would ban the practice at the state’s public universities and another that would require all schools using legacy status to pay a “public service fee” equal to a percentage of its endowment. Colleges with endowment assets exceeding $2 billion must pay at least $2 million, according to the bill’s text.

At schools like Harvard,  whose endowment surpasses $50 billion, the option to pay the penalty will make the law moot, Michael Walls, DO, MPH, president of the American Medical Student Association (AMSA), said in an interview. “Smaller schools wouldn’t be able to afford the fine and are less likely to be doing [legacy admissions] anyway,” he said. “The schools that want to continue doing it could just pay the fine.”

Dr. Walls said AMSA supports race-conscious admissions processes and anything that increases fairness for medical school applicants. “Whatever [fair] means is up for interpretation, but it would be great to eliminate legacy admissions,” he said.   
 

A version of this article originally appeared on Medscape.com.

As the cost of malpractice insurance continues to increase in many states, physicians in private practice may want to take advantage of discounts insurers offer to reduce premiums.

Getting a better deal might simply mean taking advantage of incentives and discounts your insurer may already offer. These include claims-free, new-to-practice, and working part-time discounts.

However, if you decide to shop around, keep in mind that discounts are just one factor that can affect your premium price – insurers look at your specialty, location, and claims history.

One of the most common ways physicians can earn discounts is by participating in risk management programs. With this type of program, physicians evaluate elements of their practice and documentation practices and identify areas that might leave them at risk for a lawsuit. While they save money, physician risk management programs also are designed to reduce malpractice claims, which ultimately minimizes the potential for bigger financial losses, insurance experts say.

“It’s a win-win situation when liability insurers and physicians work together to minimize risk, and it’s a win for patients,” said Gary Price, MD, president of The Physicians Foundation.

Doctors in private practice or employed by small hospitals that are not self-insured can qualify for these discounts, said David Zetter, president of Zetter HealthCare Management Consultants.

“I do a lot of work with medical malpractice companies trying to find clients policies. All the carriers are transparent about what physicians have to do to lower their premiums. Physicians can receive the discounts if they follow through and meet the insurer’s requirements,” said Mr. Zetter.

State insurance departments regulate medical malpractice insurance, including the premium credits insurers offer. Most states cap discounts at 25%, but some go as high as 70%, according to The Doctors Company, a national physician-owned medical malpractice insurer.

Insurers typically offer doctors several ways to earn discounts. The size of the discount also can depend on whether a doctor is new to a practice, remains claims free, or takes risk management courses.

In addition to the premium discount, some online risk management classes and webinars are eligible for CME credits.

“The credits can add up and they can be used for recertification or relicensure,” said Susan Boisvert, senior patient safety risk manager at The Doctors Company.

Here are five ways you may qualify for discounts with your insurer.

1. Make use of discounts available to new doctors

Doctors can earn hefty discounts on their premiums when they are no longer interns or residents and start practicing medicine. The Doctors Company usually gives a 50% discount on member premiums the first year they’re in practice and a 25% discount credit in their second year. The discounts end after that.  

Other insurance carriers offer similar discounts to doctors starting to practice medicine. The deepest one is offered in the first year (at least 50%) and a smaller one (20%-25%) the second year, according to medical malpractice brokers.

“The new-to-practice discount is based solely on when the physician left their formal training to begin their practice for the first time; it is not based on claim-free history,” explained Mr. Zetter.

This is a very common discount used by different insurer carriers, said Dr. Price. “New physicians don’t have the same amount of risk of a lawsuit when they’re starting out. It’s unlikely they will have a claim and most liability actions have a 2-year time limit from the date of injury to be filed.”

2. Take advantage of being claims free

If you’ve been claims free for at least a few years, you may be eligible for a large discount.

“Doctors without claims are a better risk. Once a doctor has one claim, they’re likely to have a second, which the research shows,” said Mr. Zetter.

The most common credit The Doctors Company offers is 3 years of being claim free – this earns doctors up to 25%, he said. Mr. Zetter explained that the criteria and size of The Doctors Company credit may depend on the state where physicians practice.

“We allowed insurance carriers that we acquired to continue with their own claim-free discount program such as Florida’s First Professionals Insurance Company we acquired in 2011,” he said.

Doctors with other medical malpractice insurers may also be eligible for a credit up to 25%. In some instances, they may have to be claims free for 5 or 10 years, say insurance experts.

It pays to shop around before purchasing insurance.

3. If you work part time, make sure your premium reflects that

Physicians who see patients part time can receive up to a 75% discount on their medical liability insurance premiums.

The discounts are based on the hours the physician works per week. The fewer hours worked, the larger the discount. This type of discount does not vary by specialty.

According to The Doctors Company, working 10 hours or less per week may entitle doctors to a 75% discount; working 11-20 hours per week may entitle them to a 50% discount, and working 21-30 hours per week may entitle them to a 25% discount. If you are in this situation, it pays to ask your insurer if there is a discount available to you.

4. Look into your professional medical society insurance company

“I would look at your state medical association [or] state specialty society and talk to your colleagues to learn what premiums they’re paying and about any discounts they’re getting,” advised Mr. Zetter.

Some state medical societies have formed their own liability companies and offer lower premiums to their members because “they’re organized and managed by doctors, which makes their premiums more competitive,” Dr. Price said.

Other state medical societies endorse specific insurance carriers and offer their members a 5% discount for enrolling with them.

5. Enroll in a risk management program

Most insurers offer online educational activities designed to improve patient safety and reduce the risk of a lawsuit. Physicians may be eligible for both premium discounts and CME credits.

Medical Liability Mutual Insurance Company, owned by Berkshire Hathaway, operates in New York and offers physicians a premium discount of up to 5%, CME credit, and maintenance of certification credit for successfully completing its risk management program every other year.

ProAssurance members nationwide can earn 5% in premium discounts if they complete a 2-hour video series called “Back to Basics: Loss Prevention and Navigating Everyday Risks: Using Data to Drive Change.”

They can earn one credit for completing each webinar on topics such as “Medication Management: Minimizing Errors and Improving Safety” and “Opioid Prescribing: Keeping Patients Safe.”

MagMutual offers its insured physicians 1 CME credit for completing their specialty’s risk assessment and courses, which may be applied toward their premium discounts.

The Doctors Company offers its members a 5% premium discount if they complete 4 CME credits. One of its most popular courses is “How To Get Rid of a Difficult Patient.”

“Busy residents like the shorter case studies worth one-quarter credit that they can complete in 15 minutes,” said Ms. Boisvert.

“This is a good bargain from the physician’s standpoint and the fact that risk management education is offered online makes it a lot easier than going to a seminar in person,” said Dr. Price.

A version of this article first appeared on Medscape.com.

As the cost of malpractice insurance continues to increase in many states, physicians in private practice may want to take advantage of discounts insurers offer to reduce premiums.

Getting a better deal might simply mean taking advantage of incentives and discounts your insurer may already offer. These include claims-free, new-to-practice, and working part-time discounts.

However, if you decide to shop around, keep in mind that discounts are just one factor that can affect your premium price – insurers look at your specialty, location, and claims history.

One of the most common ways physicians can earn discounts is by participating in risk management programs. With this type of program, physicians evaluate elements of their practice and documentation practices and identify areas that might leave them at risk for a lawsuit. While they save money, physician risk management programs also are designed to reduce malpractice claims, which ultimately minimizes the potential for bigger financial losses, insurance experts say.

“It’s a win-win situation when liability insurers and physicians work together to minimize risk, and it’s a win for patients,” said Gary Price, MD, president of The Physicians Foundation.

Doctors in private practice or employed by small hospitals that are not self-insured can qualify for these discounts, said David Zetter, president of Zetter HealthCare Management Consultants.

“I do a lot of work with medical malpractice companies trying to find clients policies. All the carriers are transparent about what physicians have to do to lower their premiums. Physicians can receive the discounts if they follow through and meet the insurer’s requirements,” said Mr. Zetter.

State insurance departments regulate medical malpractice insurance, including the premium credits insurers offer. Most states cap discounts at 25%, but some go as high as 70%, according to The Doctors Company, a national physician-owned medical malpractice insurer.

Insurers typically offer doctors several ways to earn discounts. The size of the discount also can depend on whether a doctor is new to a practice, remains claims free, or takes risk management courses.

In addition to the premium discount, some online risk management classes and webinars are eligible for CME credits.

“The credits can add up and they can be used for recertification or relicensure,” said Susan Boisvert, senior patient safety risk manager at The Doctors Company.

Here are five ways you may qualify for discounts with your insurer.

1. Make use of discounts available to new doctors

Doctors can earn hefty discounts on their premiums when they are no longer interns or residents and start practicing medicine. The Doctors Company usually gives a 50% discount on member premiums the first year they’re in practice and a 25% discount credit in their second year. The discounts end after that.  

Other insurance carriers offer similar discounts to doctors starting to practice medicine. The deepest one is offered in the first year (at least 50%) and a smaller one (20%-25%) the second year, according to medical malpractice brokers.

“The new-to-practice discount is based solely on when the physician left their formal training to begin their practice for the first time; it is not based on claim-free history,” explained Mr. Zetter.

This is a very common discount used by different insurer carriers, said Dr. Price. “New physicians don’t have the same amount of risk of a lawsuit when they’re starting out. It’s unlikely they will have a claim and most liability actions have a 2-year time limit from the date of injury to be filed.”

2. Take advantage of being claims free

If you’ve been claims free for at least a few years, you may be eligible for a large discount.

“Doctors without claims are a better risk. Once a doctor has one claim, they’re likely to have a second, which the research shows,” said Mr. Zetter.

The most common credit The Doctors Company offers is 3 years of being claim free – this earns doctors up to 25%, he said. Mr. Zetter explained that the criteria and size of The Doctors Company credit may depend on the state where physicians practice.

“We allowed insurance carriers that we acquired to continue with their own claim-free discount program such as Florida’s First Professionals Insurance Company we acquired in 2011,” he said.

Doctors with other medical malpractice insurers may also be eligible for a credit up to 25%. In some instances, they may have to be claims free for 5 or 10 years, say insurance experts.

It pays to shop around before purchasing insurance.

3. If you work part time, make sure your premium reflects that

Physicians who see patients part time can receive up to a 75% discount on their medical liability insurance premiums.

The discounts are based on the hours the physician works per week. The fewer hours worked, the larger the discount. This type of discount does not vary by specialty.

According to The Doctors Company, working 10 hours or less per week may entitle doctors to a 75% discount; working 11-20 hours per week may entitle them to a 50% discount, and working 21-30 hours per week may entitle them to a 25% discount. If you are in this situation, it pays to ask your insurer if there is a discount available to you.

4. Look into your professional medical society insurance company

“I would look at your state medical association [or] state specialty society and talk to your colleagues to learn what premiums they’re paying and about any discounts they’re getting,” advised Mr. Zetter.

Some state medical societies have formed their own liability companies and offer lower premiums to their members because “they’re organized and managed by doctors, which makes their premiums more competitive,” Dr. Price said.

Other state medical societies endorse specific insurance carriers and offer their members a 5% discount for enrolling with them.

5. Enroll in a risk management program

Most insurers offer online educational activities designed to improve patient safety and reduce the risk of a lawsuit. Physicians may be eligible for both premium discounts and CME credits.

Medical Liability Mutual Insurance Company, owned by Berkshire Hathaway, operates in New York and offers physicians a premium discount of up to 5%, CME credit, and maintenance of certification credit for successfully completing its risk management program every other year.

ProAssurance members nationwide can earn 5% in premium discounts if they complete a 2-hour video series called “Back to Basics: Loss Prevention and Navigating Everyday Risks: Using Data to Drive Change.”

They can earn one credit for completing each webinar on topics such as “Medication Management: Minimizing Errors and Improving Safety” and “Opioid Prescribing: Keeping Patients Safe.”

MagMutual offers its insured physicians 1 CME credit for completing their specialty’s risk assessment and courses, which may be applied toward their premium discounts.

The Doctors Company offers its members a 5% premium discount if they complete 4 CME credits. One of its most popular courses is “How To Get Rid of a Difficult Patient.”

“Busy residents like the shorter case studies worth one-quarter credit that they can complete in 15 minutes,” said Ms. Boisvert.

“This is a good bargain from the physician’s standpoint and the fact that risk management education is offered online makes it a lot easier than going to a seminar in person,” said Dr. Price.

A version of this article first appeared on Medscape.com.

As the cost of malpractice insurance continues to increase in many states, physicians in private practice may want to take advantage of discounts insurers offer to reduce premiums.

Getting a better deal might simply mean taking advantage of incentives and discounts your insurer may already offer. These include claims-free, new-to-practice, and working part-time discounts.

However, if you decide to shop around, keep in mind that discounts are just one factor that can affect your premium price – insurers look at your specialty, location, and claims history.

One of the most common ways physicians can earn discounts is by participating in risk management programs. With this type of program, physicians evaluate elements of their practice and documentation practices and identify areas that might leave them at risk for a lawsuit. While they save money, physician risk management programs also are designed to reduce malpractice claims, which ultimately minimizes the potential for bigger financial losses, insurance experts say.

“It’s a win-win situation when liability insurers and physicians work together to minimize risk, and it’s a win for patients,” said Gary Price, MD, president of The Physicians Foundation.

Doctors in private practice or employed by small hospitals that are not self-insured can qualify for these discounts, said David Zetter, president of Zetter HealthCare Management Consultants.

“I do a lot of work with medical malpractice companies trying to find clients policies. All the carriers are transparent about what physicians have to do to lower their premiums. Physicians can receive the discounts if they follow through and meet the insurer’s requirements,” said Mr. Zetter.

State insurance departments regulate medical malpractice insurance, including the premium credits insurers offer. Most states cap discounts at 25%, but some go as high as 70%, according to The Doctors Company, a national physician-owned medical malpractice insurer.

Insurers typically offer doctors several ways to earn discounts. The size of the discount also can depend on whether a doctor is new to a practice, remains claims free, or takes risk management courses.

In addition to the premium discount, some online risk management classes and webinars are eligible for CME credits.

“The credits can add up and they can be used for recertification or relicensure,” said Susan Boisvert, senior patient safety risk manager at The Doctors Company.

Here are five ways you may qualify for discounts with your insurer.

1. Make use of discounts available to new doctors

Doctors can earn hefty discounts on their premiums when they are no longer interns or residents and start practicing medicine. The Doctors Company usually gives a 50% discount on member premiums the first year they’re in practice and a 25% discount credit in their second year. The discounts end after that.  

Other insurance carriers offer similar discounts to doctors starting to practice medicine. The deepest one is offered in the first year (at least 50%) and a smaller one (20%-25%) the second year, according to medical malpractice brokers.

“The new-to-practice discount is based solely on when the physician left their formal training to begin their practice for the first time; it is not based on claim-free history,” explained Mr. Zetter.

This is a very common discount used by different insurer carriers, said Dr. Price. “New physicians don’t have the same amount of risk of a lawsuit when they’re starting out. It’s unlikely they will have a claim and most liability actions have a 2-year time limit from the date of injury to be filed.”

2. Take advantage of being claims free

If you’ve been claims free for at least a few years, you may be eligible for a large discount.

“Doctors without claims are a better risk. Once a doctor has one claim, they’re likely to have a second, which the research shows,” said Mr. Zetter.

The most common credit The Doctors Company offers is 3 years of being claim free – this earns doctors up to 25%, he said. Mr. Zetter explained that the criteria and size of The Doctors Company credit may depend on the state where physicians practice.

“We allowed insurance carriers that we acquired to continue with their own claim-free discount program such as Florida’s First Professionals Insurance Company we acquired in 2011,” he said.

Doctors with other medical malpractice insurers may also be eligible for a credit up to 25%. In some instances, they may have to be claims free for 5 or 10 years, say insurance experts.

It pays to shop around before purchasing insurance.

3. If you work part time, make sure your premium reflects that

Physicians who see patients part time can receive up to a 75% discount on their medical liability insurance premiums.

The discounts are based on the hours the physician works per week. The fewer hours worked, the larger the discount. This type of discount does not vary by specialty.

According to The Doctors Company, working 10 hours or less per week may entitle doctors to a 75% discount; working 11-20 hours per week may entitle them to a 50% discount, and working 21-30 hours per week may entitle them to a 25% discount. If you are in this situation, it pays to ask your insurer if there is a discount available to you.

4. Look into your professional medical society insurance company

“I would look at your state medical association [or] state specialty society and talk to your colleagues to learn what premiums they’re paying and about any discounts they’re getting,” advised Mr. Zetter.

Some state medical societies have formed their own liability companies and offer lower premiums to their members because “they’re organized and managed by doctors, which makes their premiums more competitive,” Dr. Price said.

Other state medical societies endorse specific insurance carriers and offer their members a 5% discount for enrolling with them.

5. Enroll in a risk management program

Most insurers offer online educational activities designed to improve patient safety and reduce the risk of a lawsuit. Physicians may be eligible for both premium discounts and CME credits.

Medical Liability Mutual Insurance Company, owned by Berkshire Hathaway, operates in New York and offers physicians a premium discount of up to 5%, CME credit, and maintenance of certification credit for successfully completing its risk management program every other year.

ProAssurance members nationwide can earn 5% in premium discounts if they complete a 2-hour video series called “Back to Basics: Loss Prevention and Navigating Everyday Risks: Using Data to Drive Change.”

They can earn one credit for completing each webinar on topics such as “Medication Management: Minimizing Errors and Improving Safety” and “Opioid Prescribing: Keeping Patients Safe.”

MagMutual offers its insured physicians 1 CME credit for completing their specialty’s risk assessment and courses, which may be applied toward their premium discounts.

The Doctors Company offers its members a 5% premium discount if they complete 4 CME credits. One of its most popular courses is “How To Get Rid of a Difficult Patient.”

“Busy residents like the shorter case studies worth one-quarter credit that they can complete in 15 minutes,” said Ms. Boisvert.

“This is a good bargain from the physician’s standpoint and the fact that risk management education is offered online makes it a lot easier than going to a seminar in person,” said Dr. Price.

A version of this article first appeared on Medscape.com.