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Coding Matters™ Articles
- 3 Tips to Deal with Difficult Personalities
- What Will AI Mean for the Medical Coding Industry?
- Coding Errors Cause $22.5 million in Medicare Overpayments, says OIG
3 Tips to Deal with Difficult Personalities
Challenging personalities at work can ruin your mood and slow your progress. Learn three ways to handle problematic people at work.
By Alicia Gallegos
Difficult personalities at work not only impact your daily job experience, but frequently affect the overall harmony of your team. For coders, it’s not uncommon to encounter difficult clinicians, passive-aggressive coworkers, or combative colleagues that alter morale or reduce productivity. Understanding how to effectively handle such challenging personalities is key to maintaining a thriving work environment. Here are three tips for navigating difficult personalities in the workplace.
Develop a Rapport –
- Developing a relationship with a challenging coworker or boss may seem like the last thing you want to do, but forging a bond can actually be very effective in curbing conflict, according to the Management Training Institute. Take the time to get to know the person, their style of work, and how they best communicate. Acting with genuine interest may also motivate a difficult colleague to return the effort and treat you with more respect.
If the difficult person is your boss, it’s beneficial to observe his or her behavioral style, preferences, and pet peeves, says Margie Warrell, PhD, an author and global expert on leadership.
“The more you can match your style to your boss’s style when communicating, the more he will really hear what you’re saying,” Warrell writes in this Forbes column about handling bad bosses.
- Prepare for Challenging Conversations –
It helps to prepare for challenging conversations or negotiations ahead of time. Mentally preparing will help you stay calm, raise the chances that the interaction goes smoothly, and improve the ultimate outcome, says Amy Gallo, a contributing editor at Harvard Business Review, cohost of the Women at Work podcast.
Think about the logistics of your meeting and how you will frame the problem and suggested solution, writes Gallo. If you’re feeling anxious or upset about the interaction, vent to a trusted friend or colleague beforehand. But when it’s time to meet with the difficult person, remain as neutral and positive as possible.
“Be honest with yourself about how hard the conversation might be, but also put as constructive a frame on it as possible,” Gallo writes.
- Don’t Take it Personally
When a supervisor or coworker exhibits bad behavior, it’s easy to see it as a personal attack. But taking it personally can cloud our view and trigger an emotional response, rather than a proactive approach.
Instead, focus on changing your pattern of emotional and behavioral responses to the difficult personality, advises a Northwestern University article. Depersonalize the behavior and refrain from becoming defensive.
“If you feel you’ve run out of options for dealing with [the person] reasonably, then don’t go rumor-mongering or bad-mouthing…That will ultimately say more about you than it does about your boss,” Warrell says. “Rather, follow proper procedures for registering complaints with human resources or with higher-level superiors, documenting each step of the way.”
What Will AI Mean for the Medical Coding Industry?
Artificial intelligence is rapidly changing how healthcare operates. How will AI impact coders and the landscape as they know it?
August 8, 2023
By Alicia Gallegos
Over the last year, the subject of artificial intelligence (AI) has dominated the headlines, with the technology being called “revolutionary” across the health care spectrum, particularly for the medical coding industry.
Uncertainty remains however, about whether the hype will live up to the reality, and if the benefits of using AI in coding will outweigh the risks. Questions also linger about whether artificial intelligence will enhance or deplete the coding workforce.
“I can see both positives and negatives to AI,” said Mary A. Johnson, MBA, a certified professional coder (CPC) and health information management program director at Central Carolina Technical College, based in Sumter, South Carolina.
“It’s definitely going to be a major help to improve the field, but there’s also going to be some drawbacks, as with anything.”
The Upside of AI in Medical Coding
As artificial intelligence becomes more utilized, advantages include the potential for AI to lower costs, boost revenue cycles, and streamline processes, said Johnson, who has authored four medical coding textbooks and also serves as a subject matter expert for BarChart Publishing Inc. She also foresees AI technology contributing to more detailed documentation from providers and a greater level of clarity that will ultimately improve coding.
A number of AI companies in the coding space are already touting just this. Fathom for instance, a medical coding automation platform founded in 2016, works with healthcare clients to fully automate upwards of 80% of their coding volumes direct to bill. The company also uses AI to check coders’ work to verify accuracy and flag charts that may represent potential denials or unnecessary downcoding.
“Austin Ward, head of growth for Fathom, says the company greatly reduces the total cost of coding operations. In terms of advantages, it’s really the trifecta- AI is faster, cheaper, and more accurate,” he said. “In terms of growth of the [AI in coding] industry, in the last 18 months, it’s been pretty extraordinary.”
The rising shortage of medical coders has been a driving force behind the enormous growth, he said. Prior to the pandemic, the average age of a coder in the U.S. was 54 and most coders were/are women, Ward noted. During the pandemic, women age 55 to 64 women had the highest rate of workforce abandonment. The increasing shortages have led to a sharp demand for automated coding services.
New York-based SmarterDx, founded in 2019, meanwhile, provides clients an automated pre-bill review platform that uses AI to identify diagnosis codes from clinical data, including notes, labs, medications, and orders. The company scans charts and helps hospital teams look for any missed documentation or coding opportunities findings prior to billing, explains Michael Gao, MD, SmarterDx CEO.
Gao says customers generate an average of $2 million per 10,000 discharges in additional revenue.
“Most AI companies position their value as cost-reduction, aka labor force reduction, and there’s a lot of anxiety around this,” Gao said. “However, our value is finding more revenue by helping existing teams do a better job. We believe AI can augment people and allow them to work more accurately and more efficiently, in the same way that Excel makes accountants more valuable, rather than obsolete.
The Downsides and Risks of AI
Whether AI will replace human coders and cause marked job losses for the industry is a significant concern, says Johnson.
“As we bring and mechanize many of our processes in health care, it’s going to eliminate the need for certain positions,” she said. “We have to seriously sit down and ethically think about, what is this doing to our workforce? Can we reposition [coding staff] into another part of the facility? I worry about creating unemployment and people losing their positions.”
Ward emphasizes that AI can’t operate alone, and that human coders are still well needed to monitor and oversee the technology, as with all any vendor.
“But I’m not going to sugar coat it, there will be far fewer medical coding jobs in the future,” he said. “But I think the [jobs that do exist] will be much more exciting, more interesting jobs than there have been historically.”
Training challenges may be another consequence of AI use. With artificial intelligence tools comes necessary training for health care providers, clinical, and administrative staff on how to use the technology efficiently and effectively, Johnson said.
“Our staffs and providers are wonderful, but they’re already stressed to the limit,” she said. “When we add more training in, that’s just adding another chip to that pile. That’s another problem we’ll have to overcome.”
Another risk is the potential for security breaches associated with AI. As more sensitive data is created, received, and stored in large data quantities, it broadens the possibility for cyberattacks, according to Jon Moore, chief risk officer and head of consulting services and customer success of Clearwater, a cybersecurity firm.
“Bad actors can and will attack vulnerabilities anywhere along the AI data pipeline,” Moore wrote in a recent editorial for Medical Economics. “Another risk is the unique privacy attacks that AI algorithms may be subject to, including membership inference, reconstruction, and property inference attacks. In these types of attacks, information about individuals, up to and including the identity of those in the AI training set, may be leaked.”
Looking ahead, there’s no doubt AI will be part of medical coding’s future. If used appropriately, Johnson believes the technology will greatly benefit all aspects of health care.
“We have to be a team. We have to remember we are the human beings. AI is a tool to use,” she said. “As long as we use it responsibly, ethically, and legitimately, then it will be a magnificent tool.”
Recently, one of Betsy Nicoletti’s team members asked an AI program to write a summary of the 2024 Proposed Physician Fee Schedule Rule. “The summary was pretty accurate,” Nicoletti said. “Of course, it lacked the detail and nuance we need to submit claims, but it wasn’t bad.”
Coding Errors Cause $22.5 million in Medicare Overpayments, says OIG
July 31, 2023
By Alicia Gallegos
Miscoded claims led to $22.5 million in Medicare overpayments for physician services, an investigation by the Office of Inspector General (OIG) has found.
The overpayments resulted from incorrect place-of-service codes that caused Medicare to pay higher non-facility rates, rather than lower facility rates, for physician services provided to enrollees who were Part A skilled nursing facility (SNF) or hospital inpatients. During the two-year period analyzed, the Centers for Medicare & Medicaid Services (CMS) made overpayments totaling $22,463,193 for 1,130,182 claim lines by paying the non-facility rate for these physician services, the OIG recently reported. (Skilled nursing facility POS 31, for a Part A covered stay, is paid at the facility rate; nursing facility POS 32 for a non-Part A covered stay or nursing facility is paid at the non-facility rate.) Also, although generally the correct place of service is where the patient is seen, there is an exception. If a patient is an inpatient or in a SNF with Part A coverage but physically goes to the physician office, the POS should be inpatient or SNF.
Because of the incorrect codes, CMS paid twice —compensating both the SNF and the practitioner for the practice expenses associated with furnishing the service. Additionally, enrollees incurred as much as $5.7 million in additional cost sharing for deductibles and coinsurances, according to the OIG’s findings.
The coding errors went undetected because CMS did not have Common Working File (CWF) system edits that could identify the mistakes. While the CWF system contains both prepayment and post payment system edits intended to prevent or detect overpayments, the system does not have edits that compare Part A inpatient claims to Part B claim lines to assess the proper place-of service code on the Part B claim line, and the corresponding payment rate to the practitioner while an enrollee is an inpatient. CWF edits are also limited to the fields available on a claim, which do not include fields indicating when a patient leaves and returns on the same day.
CMS has expressed reluctance to take enforcement action for these claim lines because the agency contends that neither statute nor CMS regulations specifically address situations in which a SNF or hospital inpatient leaves to receive a physician service in a non-facility setting, according to the OIG’s report.
In its guidance, the OIG recommends that CMS recover the $22.5 million in overpayments and notify the appropriate practitioners so they can identity, report, and return any overpayments in accordance with the government’s 60-day rule. The OIG would also like to see CMS establish and apply CWF edits to detect instances in which practitioners incorrectly use the non-facility place-of-service code for a SNF while an enrollee is a Part A SNF inpatient. Other recommendations include that CMS consider developing a mechanism for facilities to indicate when an inpatient leaves a facility and returns the same day and provide additional education to practitioners on the appropriate use of place-of-service codes.