Friday, February 10, 2023

How physicians can give negative feedback

Giving negative feedback can be one of the most difficult parts of any job. For most physicians, negative feedback is not part of patient care, but it can be part of working with staff and colleagues. Giving negative feedback is different than discussing a bad prognosis with a patient because negative feedback can be an associated sense of rejection and blame. While few doctors could actually enjoy the process of giving negative feedback, it can be a less dreaded interaction if it’s done with some thoughtful planning.


Establish roles and purpose


When you give negative feedback, the recipient of your assessment might push back by arguing that you are wrong or that you are not qualified to provide an opinion about their work. As a physician, before you provide your feedback in the work setting, it’s important to verify that you are in a position to give this type of appraisal. Whether you are formally supervising other doctors or assessing one of your peers, defining your roles and the need for the critique is a vital first step.

Perhaps your evaluation is scheduled, which can make the process easier. But if you are giving unscheduled feedback to a peer for the purpose of getting fair treatment for yourself, improving the process for your team, or responding to a patient care issue, you need to be clear about several things:
  • Why this feedback is important
  • Why you are the one who is giving the feedback
  • Why feedback is needed at this time
  • The overall purpose and desired outcome
  • The traditional sandwich still works

When it comes to negative feedback, using the 1/3 rule is a tried-and-true strategy, particularly for people who might be highly sensitive to criticism. Opening with praise, then clearly stating the criticism, and then closing with praise is a way to present the matter as being only one aspect of a person’s whole performance.

Sometimes, pointing out the positives can prompt a reassessment. It might lead to organizing assignments so that people can increase the amount of time they spend on tasks they are especially good at. If this is something that you and your whole medical team would consider to be productive, the complement/criticism sandwich might frame the situation for you and others to see how to better apply colleague talents.


Provide suggestions


An important aspect of feedback is giving suggestions for improvement. Sometimes the path to improvement is easy to see and spell out, but other times you might not have an idea of how a

person can improve. If the steps to fixing the problem are not clear, your colleague may need professional help. For example, a physician might benefit from a continuing medical education (CME) program with instructions covering the area of deficit. Or they might need a more comprehensive or personalized program to help them work through problems that are interfering with their work. Suggestions might not be simple, but it is a good idea to discuss actionable steps to remedy the situation at an early stage.


Show realistic belief in improvement


Being criticized can be overwhelming, even for physicians who seem to be confident. If you think your colleague can improve, it helps to directly tell them that you believe they can fix the problem. They might not be able to see their potential until you point it out to them.

Feedback about a deficit is usually about the action, not the person. This is the case most of the time. However, if you and colleagues are concerned that a peer physician or another health care professional might not ever be able to achieve the improvement necessary to provide good patient care, it could be beneficial to use objective measures to make this assessment and to find resources so they can find another path that doesn’t endanger patient safety.


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Thursday, February 9, 2023

AI's impact on the common health conditions affecting seniors

Artificial intelligence is arguably one of the most important technologies being used in healthcare today. I could talk forever about AI's significance in healthcare, from its ability to predict treatment protocols based on various datasets to its automation of administrative tasks. But one of the most powerful impacts of artificial intelligence, which will be the focus of this article, is how it's being used to prevent, detect, and treat some of the most common health conditions affecting elderly patients.

The list of health conditions impacted by AI is pretty extensive. Diabetes, cancer, strokes, and sleep apnea are just a few areas where we're seeing substantial improvements in patient outcomes. In this article, I want to take a look at four other conditions experiencing a similar level of disruption thanks to artificial intelligence: falls, sepsis, cardiovascular issues, and dementia.
Predicting and preventing falls

One in four seniors fall every year in the United States. That means around 36 million falls are reported, resulting in over 32,000 deaths. It may be surprising to hear that falls are actually one of the leading cause of fatal injury in older adults. This is a scary statistic for a condition that can be easily prevented with the right interventions, like artificial intelligence

Researchers at two universities in North Carolina used cameras and virtual reality systems to record 30 different body markers on elderly patients as they walked on a treadmill. Visual cues were introduced that suggested the motion of swaying or falling so that the technology was able to identify if the patient had any balance impairments that were previously undiagnosed. This diagnosis made it possible for facilities and caregivers to know which patient was a fall risk and would need further monitoring.

Skilled nursing facilities and other long-term care centers also have access to AI-enabled technology like UC Berkeley Artificial Intelligence Research Labs' SafelyYou, a company that installs cameras in patient rooms which immediately alert staff the moment a fall is detected. This has been proven to reduce falls by 40 percent and lower emergency room visits by approximately 80 percent.


Demystifying and combating sepsis


Sepsis is commonly undetected or misdiagnosed, especially in older adults, who become more susceptible to sepsis as they age. Sepsis claims one in five lives each year, so early detection is critical in saving lives.

There is still so much mystery surrounding sepsis, which is why it goes undetected. Its earliest symptoms, like confusion and fever, can easily be attributed to other conditions, so technologies like artificial intelligence are powerful tools in getting patients the diagnosis and treatment they need as quickly as possible.

John Hopkins University recently developed a new AI system that has reduced sepsis deaths by around 20 percent. Their Targeted Real-Time Early Warning system combs through a patient's medical notes, clinical records, and lab results to identify if someone is at-risk of developing sepsis. It then recommends treatment protocols depending on the patient, such as starting antibiotic treatment, and keeps all the healthcare staff involved in that patient's care journey informed of what's going on. According to their data, the AI was accurate nearly 40 percent of the time, which is far more advanced than even just a few years ago.


Detecting overlooked cardiovascular diseases


Adults over the age of 65 are more likely to suffer from cardiovascular disease than younger generations, although heart problems are one of the most common conditions affecting the global population and also one of the leading causes of death. For this reason, there's been a major push for using AI to help predict and prevent cardiovascular problems. And a lot of incredible opportunities have come from this.

Physician-scientists at the Smidt Heart Institute have developed an AI tool that helps medical staff identify two major life-threatening heart conditions that often get missed: cardiac amyloidosis and hypertrophic cardiomyopathy. Algorithms can pinpoint diseases that can't be seen by the naked eye and then use that information to inform a diagnosis so treatment can happen immediately. Other researchers from Cedars-Sinai created a tool that can accurately predict which patients will likely experience a heart attack within five years. Additionally, the National Health Service created a tool that detects heart problems within 20 seconds while patients are getting an MRI can.


Helping patients with dementia


Another common condition among the aging population, dementia is a frightening and heartbreaking disease. Changes in your brain can happen over 20 years before any symptoms even appear, which weakens the effects of treatment.

Artificial intelligence can help healthcare employees see signs of cognitive decline that are indistinguishable to the human eye. One AI tool developed by researchers at the Stevens Institute of Technology can diagnose patients with dementia with 95 percent accuracy, which is significant since around 40 to 60 percent of adults who have dementia usually never receive a diagnosis at all. The earlier and more accurately dementia can be predicted, the earlier measures can be taken to improve patient outcomes and quality of life.

The impact of artificial intelligence is vast, but it's clear that one of its biggest benefits is making life-saving care even more tangible in the healthcare sector.


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Wednesday, February 8, 2023

Caring must be prioritized over coding

Unlike traditional Medicare fee-for-service plans, Medicare Advantage (MA) programs receive a capitated monthly payment for an individual beneficiary’s cost of care that is partially based on a risk score, which increases or decreases depending on an enrollee’s health compared to a national average.

Health plans offer greater financial incentives to providers for more thorough documentation and coding of beneficiaries’ diagnoses. In other words, higher risk scores translate to higher payments. Naturally this leads to instances of payers gaming the system with so-called “upcoding” or “uncorrected coding intensity.”

The practice has become so widespread that CMS currently applies a 5.9% reduction to MA risk scores to counter the effect. Now, the Medicare Payment Advisory Commission (MedPAC), which advises Congress on Medicare payment issues and is currently focused on improving the accuracy of MA risk adjustment, is pushing to rein in excess payments to MA plans.

The practice has not gone unnoticed by the Department of Justice, which is taking action against MA plans that artificially inflate MA payments. In 2020, the commission found MA risk scores were about 9.5% higher than those for comparable beneficiaries in traditional Medicare because of coding intensity.

It’s all about the codes…Or is It?


Today there is ample advice and guidance in the industry counseling providers and health plans on how to best optimize risk-adjusted coding to qualify for higher risk scores.

Unfortunately, all too often the one consideration that is omitted from these conversations is the basic fact that this is not a matter of simply picking a code and being done with it. The supporting clinical documentation not only must reflect the selected diagnosis, but it must also show that the condition was appropriately assessed and treated. The inclusion of more specific or additional codes alone will not be sufficient when the auditors arrive.

All this emphasis on “optimized” coding raises a very fundamental question about providers’ (and payers’) primary focus: Is the coding taking priority over the caring? At face value, the question may sound absurd, but the escalating attention paid to the coding makes it a valid consideration.



The role of legacy technology


Now consider the role of the electronic health record (EHR) in this situation. After all, the EHR is the first destination for all these codes and should help guide clinical actions. At the point of care, the EHR is supposed to serve as a tool to help the clinician assess and document the patient’s conditions and the prescribed treatment. But all too often, they must spend an inordinate amount of time searching through volumes of disjointed data to get what they need. With so much disorganized information, is it any wonder that clinicians struggle to find relevant history about their patients’ existing or suspected problems?

So, when you think about it, the EHR is little more than an inert code repository primarily employed to justify diagnoses and billing––rather than to empower clinicians to improve patient care. We’ve been mistakenly focused on the codes and coding for too long. What good is getting the code right, managing codes, managing lists of codes, or managing the updates of codes, if we’re not leveraging the technology we already have to improve patient outcomes?

The focus should be on using these tools to help the clinician manage the patient.

It is ridiculous to accept the thought of the EHR as a clinically inert obligation, but that pretty much sums up its current value. Consider the alternative––converting EHRs into diagnostically interactive tools that clinicians use to get instant access to all the clinical information related to a patient’s problem. And what if those same tools could also help satisfy the quality measures and documentation requirements with customized workflows? Suddenly, clinicians would be more empowered to improve care.

This is important because it’s not about the coding. It’s about the caring.

With the growth of MA and other risk-based incentive programs, and the increased focus on pay-for-performance and quality, providers are increasingly charged with moving the needle on patient care. Having the right code might help with getting paid, but it won’t move the care needle.

As value-based care models continue to gain acceptance, clinicians will increasingly need access to diagnostically-focused views for each patient––including each of their medical issues. They shouldn’t be required to randomly search through disparate sections of clinically inert EHRs to find the information they need.

We need to emphasize care over coding, and by transforming EHRs from static code repositories to diagnostically interactive tools to help coordinate care. Optimizing EHRs in this way will do more than just satisfy regulatory requirements and ensure proper compensation; it will also empower clinicians to deliver optimal care.


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