The Top 4 Tips to Code Medical Time-based Services Appropriately …

Many services within the CPT codebook include a time component. Except where CPT guidelines state otherwise, follow these four tips to ensure you’re reporting time-based services correctly.

1. Count only included services

When calculating time spent performing a procedure or service, include only those items specifically detailed in the code descriptor. For example, when reporting critical care services (e.g., 99291-99292), you may include the time spent interpreting cardiac output measurements or chest X-rays, performing ventilatory management or vascular access, and other services enumerated within CPT as inclusive of critical care. You may not count toward critical care the time spent performing other, separately reportable services (e.g., endotracheal intubation for airway support, 31500).

Carefully review CPT guidelines and code descriptors to determine which activities you may count toward the time of a particular service. Each code category or descriptor may include different components within a time-based code. For instance, critical care includes floor/unit time, in addition to time spent at a patient’s bedside. I contrast, when calculating time for prolonged services 99354-99357, you may count only “face-to-face” time. Many time-based services include only that time spent “face to face” with the patient. Count time away from the patient only if the code descriptor or other CPT guidelines specifically allow you to do so.

Bonus tip: As a best practice, when providing time-based services, you should document start and stop times, as well as the total time of service.

2. Pass the “midpoint” before billing a time-based service

If a code describes the “first hour” of service, you must provide and document at least 31 minutes of service. Likewise, if the unit of service is 30 minutes, you must perform and document at least 16 minutes of service (and so on). If the service does not meet the minimum time required, either you may not separately report the service, or you should report an (other) appropriate evaluation and management service code. For instance, if you provide fewer than 30 minutes of critical care (99291), CPT instructs you to report “appropriate evaluation and management codes.”

Some codes describe “24-hour services.” In most cases, you must document at least 12 hours of service to report such codes. For services lasting fewer than 12 hours, you may need to append modifier 52 reduced services. Be sure to review CPT guidelines before assigning codes or modifiers.

3. Select the “closest” code

Per CPT guidelines, “When codes are ranked in sequential typical times and the actual time is between to typical times, the code with the typical time closest to the actual time is used.”

This rule applies when reporting evaluation and management services using time — rather than the key components of history, exam, and medical decision-making — as the determining factor in the level of service (e.g., if counseling and/or coordination of care comprise more than half the encounter). In such cases, you should use CPT “reference times” to determine an appropriate evaluation and management service level.

For example, a Level 3 established patient outpatient visit (99213) has a reference time of 15 minutes, and a level 4 service (99214) has a reference time of 25 minutes. When reporting a time-based evaluation and management service lasting 19 minutes, you would report 99213 because it has the closest reference time.

4. Use the initial DOS for continues services

CPT states, “For continuous services that last beyond midnight, use the date in which the service began and report the total units of time provided continuously.”

For instance, if intravenous hydration begins at 10:30 p.m. and lasts until 1:30 a.m. the next calendar day, you would report 96360 Intravenous infusion, hydration; initial, 31 minutes to 1 hour once and 96361 …each additional hour (List separately in addition to code for primary procedure) twice. You would not report a new “initial” service (96360) on the new calendar date, unless that service truly represents a different session or encounter with the patient.

G. John Verhovshek, MA, CPC, is managing editor at AAPC, the nation’s largest training and credentialing organization for the business side of healthcare.

Staying Updated On Cpt Code Changes – InformationBible

It’s of critical importance for health care professionals and medical personnel to remain up to date on all changes to the CPT Code. The CPT code is the Current Procedural Terminology code set, and it is managed by the American Medical Association (AMA). The AMA appoints a committee known as the CPT Editorial Panel to handle it, which is used by a variety of health care specialties to make communication of medical terms consistent and uniform. It allows patients, physicians, organizations, and payers to communicate clearly and consistently in regards to treatments.

Changes to the code released in new editions which are available annually. These new additions come out each year in October. There are standard and professional editions of the CPT code. Unlike the ICD-9 and ICD-10 code sets, the CPT set does not refer to diagnosis of conditions but rather to the treatments and services used by medical professionals in the treatment of their patients. While the ICD code sets do have some codes for this purpose, they are not utilized in outpatient settings, while CPT codes are. The CPT code set is known as level one of the health care procedure coding system, and identified as such by the Centers for Medicare and Medicaid Services, and thus is very important for all practitioners.

The code set is divided in a number of different categories. Category I consists of codes used for evaluation and management, anesthesia, surgery, radiology, pathology and laboratory, and medicine. Each of these subsections is broken down in a logical and intuitive manner so that professionals in their respective fields can identify which type of code is being
used. For example, the codes for evaluation and management range from 99201 to 99499. The codes for anesthesia fall into two groups, 00100 to 01999 and 99100 to 99150. Those for radiology range from 70010 to 79999. Category II codes are related to composite measures, patient history, physical exams, screening processes, results, preventative interventions, follow-ups, patient safety, and structural measures. Category III codes are reserved for emerging technologies.

While the CPT code set is required to be used by nearly all insurance (health care) payment systems as well as most medical practice management solutions, it is the copyrighted intellectual property of the American Medical Association, as determined by the case Practice Management versus American Medical Association. Even the Centers for Medicare and Medicaid Services (CMS) requires the use of the codes, as do practical applications of the Health Insurance Portability and Accountability Act (HIPAA). Although the codes appear in the Federal Register, the AMA’s copyright requires that most organizations, practitioners, and facilities that use the code pay fees for licenses required to access it. However, there are limited search capabilities related to the code available on the American Medical Association website. These searches are not intended for use by commercial organizations, only for individual, personal use. CPT Code changes are also announced on the website, in abbreviated form.For up-to-date information on CPT code, you may visit the following website:CPT Code Changes

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OIG "Top Ten" List Costs Hospital Nearly $10 Million : Bridging …

 Nothing strikes fear in the heart of a hospital compliance officer like receiving a medical record request from the Office of Inspector General (OIG). Now, with the OIG’s recent report on its review of Medicare billing by a Midwestern academic medical center, the prospect of such a review is even more terrifying. 

Based on an extrapolation of its findings from a review of 228 claims, the OIG concluded the University of Cincinnati Medical Center (UCMC) owes $9.8 million for overpayments received in 2010 and 2011.

For reasons not explained in the report, the OIG chose to review “Top Ten” claims submitted by UCMC in 2010 and 2011. Through computer matching, data mining, and data analysis techniques, the OIG has identified the following ten types of hospital inpatient and outpatient claims that pose a high risk for noncompliance with Medicare billing requirements: 

1.           Inpatient short stays

2.           Inpatient claims paid in excess of charges

3.           Inpatient claims billed with high-severity-level DRG codes

4.           Inpatient and outpatient manufacturer credits for replaced medical devices

5.           Inpatient transfers

6.           Inpatient psychiatric facility (IPF) emergency department adjustments

7.           Inpatient claims for blood clotting factor drugs

8.           Outpatient claims with payments exceeding $25,000

9.           Outpatient claims billed with evaluation and management (E&M) services

10.       Outpatient claims billed for Doxorubicin Hydrochloride

According to the report, Medicare paid UCMC $256 million for 16,674 inpatient and 98,043 outpatient claims during this two-year period. The OIG’s audit covered $22.8 million in Medicare payments for 2,742 “Top Ten” claims submitted by UCMC in 2010 and 2011.  

Of the total number of “Top Ten” claims, the OIG selected for review a stratified random sample of 228 claims (169 inpatient and 59 outpatient) with payments totaling $3.3 million. The OIG auditors found billing errors on 127 of the 228 claims and calculated the net overpayment as $603,267. Nearly all of the overpayment was attributable to inpatient billing errors; only one-half of 1% related to outpatient billing errors.

With respect to inpatient billing, the vast majority of the overpayment – nearly $400,000 – related to short stays that should have been billed as outpatient rather than inpatient services. The OIG noted that UCMC “may be able to bill Medicare Part B for all services (except for services that specifically require an outpatient status) that would have been reasonable and necessary had the beneficiary been treated as a hospital outpatient rather than admitted as an inpatient.” The OIG, however, did not reduce the total overpayment, noting it would not have enough information to do so unless and until such Part B services are billed by the hospital and adjudicated by the Medicare administrative contractor.  

Two other “Top Ten” errors had significant price tags: claims paid in excess of charges ($153,000) and claims billed with high-severity-level DRGs ($130,000). Other identified errors included manufacturer credits for replaced medical devices, transfers, and psychiatric facility emergency department adjustments.

Without an explanation of how it made the calculation, the OIG extrapolated that UCMC received net overpayments totaling at least $9,818,296 for 2010 and 2011. This represents nearly half of the total amount UCMC received in payment for “Top Ten” claims during the same period.

The OIG expects UCMC to repay this nearly $10 million to the Medicare program. Presumably, the total overpayment will be reduced somewhat once UCMC bills and receives payment for Part B services as explained above, but the amount remains staggering.

According to the OIG, “[t]hese errors occurred primarily because [UCMC] did not have adequate controls to prevent the incorrect billing of Medicare claims within the selected risk areas that contained errors.” For the short stays, the OIG identified three specific weaknesses in UCMC’s internal controls: (1) lack of documentation to support the physicians’ clinical decisions to admit patients; (2) physicians’ non-receptiveness to the involvement of case managers; and (3) interpretation of third-party vendor services.

With respect to improper billing for high-severity-level DRG codes, the hospital acknowledged some of these errors were the result of mistakes made by hospital staff. Although mistakes were made on a small number of claims, the alleged lack of adequate oversight had a significant financial impact given extrapolation of the error rate over a larger number of high-dollar claims.    

For UCMC, the OIG’s “Top Ten” list billing errors stands to cost the organization nearly $10 million, an amount hospital officials claim will have a “devastating effect” on its operations. In light of the OIG’s extrapolation of audit results, the importance of proper documentation, coding, and billing procedures cannot be overstated.  

PYA can assist your organization in evaluating and enhancing internal controls and processes around each of the OIG’s “Top Ten” billing errors. Our professionals can assist in resolving internally identified overpayments and provide support in anticipation of and during government audits and investigations. For more information, please contact Nancy McConnell or Denise Hall at PYA, (800) 270-9629.

Case Study: Maximizing the Response of Fire Services to Medical …

The City of Kawartha Lakes, Ontario, Canada, was formed in 2001 by a Provincial Order to amalgamate 16 lower-tier municipalities and one upper-tier county into one corporation. Although called a City, the municipality is mostly rural. The City is approximately 3,059 km2.City of Kawartha Lakes logo

Due to its large rural area, the City has 21 fire stations, comprised of both full-time fire fighters and volunteer fire fighters (VFFs). (Note: Although called volunteers, VFFs receive a minimum two hours of pay for responding to calls.) Of the 21 stations, 19 are staffed predominantly with volunteers.

City of Kawartha Lakes

City of Kawartha Lakes

When a call for assistance is made to the central 911 center, the call is directed to the individual fire stations. Each volunteer has a one-way radio and responds to a call by going to the fire station.

A significant amount of variation existed in the number of VFFs responding to medical calls. Existing City guidelines required four VFFs to respond on-scene to any medical call; however, in a two-year period, more than four VFFs responded to 77 percent of calls. In addition to this over-response, in 17 percent of calls, VFFs were not even required upon their arrival (for example, an ambulance arrived first and tended to any medical needs).

Medical calls include both high-acuity and non-life threatening calls. The City wanted to determine the cost of VFFs’ attendance at non-life threatening calls and to review the necessity of the VFF response.

Project Goals

In this improvement project, the City wanted to:

  1. Implement a consistent process for fire fighter deployment to medical calls across all fire stations. (Was the City overpaying for a poorly managed process?)
  2. Ensure the City matched the level of resources provided to the level of service required.

City of Kawartha Lakes

City of Kawartha Lakes

At this point, the first step for the City was to review its cost of poor quality. In 2012 and 2013, 77 percent of medical calls resulted in more than four VFF responding. The cost of having more than 4 VFFs attend these calls – of this excess in response – was $131,140 annually. The cost of sending VFFs to non-life threatening calls during the same period was $130,648 annually.

The cost of vehicle fuel, maintenance and repairs associated with attending non-life threatening calls – in addition to false alarms (medical aid not required once on scene or a true patient false call) – was $74,400 annually during the two-year period under review.

Review Current Process – Descriptive Statistics

Descriptive statistics – a method of statistical analysis of numeric data, discrete or continuous, that provides information about centering, spread and normality – was used to measure and establish the baseline for:

  • The number of VFFs attending a medical call
  • The proportion of medical calls based upon acuity or level of condition
  • The total number of medical calls on an annual basis

The descriptive statistics analysis identified a median of seven VFFs responding per call. The baseline measure showed that nearly half of all calls were recorded in a way that made it difficult to ascertain the acuity of the medical call based on the coding in the system that included the following options: “not required on arrival,” “medical false alarm,” “medical call no action required” or “other medical call.”

Figure 1: Control Chart – Number of VFF Responding to Calls

Figure 1: Control Chart – Number of VFF Responding to Calls

The control chart in Figure 1 displays a process that is erratic, as evidenced by the number of times the process fails (indicated by the red circles). Predicting the future performance of the process was therefore impossible.

Figure 2: Cause-and-Effect Diagram (Click to Enlarge)

Figure 2: Cause-and-Effect Diagram

The starred points in Figure 2 reflect the root causes that needed to be addressed in the future process for deployment of VFFs to medical calls. The root causes were determined at a team brainstorming session.

Analyzing the Process

Next, the team wondered if certain fire stations had a higher turnout of VFFs to calls than others.

The cause-and-effect analysis (Figure 2) identified that there was no active management of the number of VFF attending a call (see Manpower branch). A Kruskal-Wallis one-way analysis of variance test was performed here since there was continuous data with independent samples. The Kruskal-Wallis analysis proved that there is a difference in the number of VFFs attending based upon fire station location.

After this, the team looked at whether VFFs were arriving after an ambulance was already on scene (which then precluded the need for fire fighters).

The Z value estimate was used to test for the proportion of times in the future that VFFs would arrive on scene ahead of an ambulance. This test was performed because in the cause-and-effect diagram (Figure 2), VFFs were noted as attending medical calls when an ambulance was already on scene and was therefore a waste of manpower, machinery and material.

The resulting Z value estimate indicated a 56 percent to 66 percent probability that VFFs will arrive ahead of the ambulance at future calls (Table 2). Therefore, 34 percent to 44 percent of the time when the VFF attends a medical call they are creating a duplication of service, which is an unnecessary cost to the municipality.


Selecting and Testing Solutions

With the problems identified and measured, the team next embraced the Pugh matrix (Figure 3 below) – a decision-making tool used to formally compare concepts based on customer needs and functional criteria. Using that tool, the City determined that the optimum solution for how to manage the number of VFFs responding to medical calls was to enact steps consistently among all fire stations in conjunction with the central 911 operators:

  1. Have VFFs advise fire dispatch once they have the sufficient crew to respond
  2. Have fire dispatch page all remaining VFFs to let them know that their assistance is no longer required (after the initial complement of four VFFs arrive at the station in response to emergency calls)
  3. Set a maximum of seven VFFs per call (four to deploy with the fire truck and a limit of three to remain at the fire hall for paid duty time – those three may have received the call and responded before the stand-down call went out)
  4. Have the station coordinator monitor and manage the VFFs remaining at the fire hall.

Figure 3: Pugh Matrix Evaluation of Solutions for Managing VFFs (Click to Enlarge)

Figure 3: Pugh Matrix Evaluation of Solutions for Managing VFFs

Controlling the New Process

After the new process was implemented, a control plan was used to complete the project work and hand off the improved process to the process owner (the executive assistant to the Fire Chief), with procedures for maintaining the gains. The implementation process required some convincing by the Fire Chief to the volunteer fire fighters and to the City Council. Council members needed assurance that no patient would be left vulnerable and in need of care. The Paramedic Chief spoke to Council as the subject matter expert to assure Council that all calls were being monitored and that at any time, a paramedic supervisor had the authority to call in the fire services if need be.

Figure 4: Error-proof Key Input Variables (Click to Enlarge)

Figure 4: Error-proof Key Input Variables


During the project measurement phase, the baseline median was 7. With the process improvements implemented starting January 6, 2014, the median as of March 14, 2014, had decreased to 5. The percentage of medical calls that VFFs are responding to has been reduced by approximately 45 percent, since VFFs now attend only high-acuity medical calls. The new process to monitor and control the number of responding VFFs – as well as the types of calls VFFs respond to – resulted in initial savings of $43,944 for the first two months of 2014, with projected annual savings of $261,788.

Top Billing: Meet the Doctors Who Charge Medicare Top Dollar for …


from Pacific Standard

Office visits are the bread and butter of many physicians’ practices. Medicare pays for more than 200 million of them a year, often to deal with routine problems like colds or high blood pressure. Most require relatively modest amounts of a doctor’s time or medical know-how.

Not so for Michigan obstetrician-gynecologist Obioma Agomuoh. He charged for the most complex—and expensive—office visits for virtually every one of his 201 Medicare patients in 2012, his billings show. In fact, Medicare paid Agomuoh for an average of eight such visits per patient that year, a staggering number compared with his peers.

Doctors and other health providers nationwide charged the top rate in 2012 for just four percent of office visits for patients they had seen before. But Agomuoh was one of more than 1,800 health professionals nationwide who billed Medicare for the most expensive type of office visits at least 90 percent of the time that year, a ProPublica analysis of newly released Medicare data found.

Dr. John Im, who runs a Florida urgent care center, charged the program at that level for all 2,376 visits by his established patients. Kaveh Farhoomand, an Oceanside, California, internist facing disciplinary charges from his state medical board, collected the highest rate to see almost all of his 301 Medicare patients an average of seven times each.

By exposing such massive variations in how doctors bill the nation’s health program for seniors and the disabled, experts said, ProPublica’s analysis shows Medicare could—and should—be doing far more to use its own data to sniff out cost-inflating errors and fraud.

“I think this is a smoking gun,” said Dr. Robert Berenson, a former senior Medicare official who is now a fellow at the Urban Institute, a Washington, D.C., think tank. “Who’s asleep at the switch here?”

The Centers for Medicare and Medicaid Services, which runs Medicare, declined an interview request and said in a statement that it could not comment on ProPublica’s analysis because it had not seen it.

“CMS is working to ensure that physicians and health care providers appropriately bill” for office visits, part of a category known as evaluation and management (E&M) services, the agency said. “Some providers have sicker patients, thus are more likely to bill at E&M coding levels that carry higher payments. Every day we work with providers to make patient care the priority, and at the same time ensure they use E&M codes that reflect the level of service provided.”

The agency also said “it would be highly unusual for a provider to knowingly use the highest E&M billing code for all or nearly all of his or her outpatient visits.”

American Medical Association President Dr. Ardis Dee Hoven cautioned that billing data can be misleading without considering further details about doctors’ practices. Even those who handle medical billing professionally sometimes disagree about the right way to classify a visit.

Agomuoh, Im, and Farhoomand insist that they treat older, sicker, or more difficult patients than their peers. Agomuoh also suggested that the Medicare data contained errors; the agency stands behind it.

Individually, office visits for established patients cost taxpayers little, ranging from an average of $14 for the simplest cases to more than $100 for the most extensive. But collectively, they add up. Medicare shelled out more than $12 billion for them in 2012. Agomuoh received $174,000 for the visits he billed at the top rate alone, tens of thousands of dollars more than he would have taken in if his charges were more in line with his peers’.

In April, Medicare released data showing 2012 payments for outpatient services, and for the first time specified how much money went to individual health providers. Since then, most of the attention has focused on doctors who made the most from the program.

Looking at raw numbers, though, can unfairly flag some doctors who have multiple providers billing under their IDs or who justifiably use expensive services. It can be more revealing to look at which procedures doctors are performing and how frequently, and how their billings compare with those of their peers.

Office visits are a case in point. Doctors or their staffs determine how to bill for a visit based on a variety of factors, including the thoroughness of the review of a patient’s medical history, the comprehensiveness of the physical exam, and the complexity of medical decision-making involved. The AMA’s coding system gives them five options.

An uncomplicated visit, typically of short duration, should be coded a “1″; a visit that involves more intense examination and often consumes more time should be coded a “5.” The most common code for visits is in the middle, a “3.”

ProPublica focused its analysis on the 329,500 physicians and other providers who charged for at least 100 office visits for established patients. (Medicare did not release data on services that a provider performed on fewer than 11 patients.)

We found that while most providers had a tiny percentage of Level 5 cases, more than 1,200 billed exclusively at the highest level. Another 600 did it more than 90 percent of the time. About 20,000 health professionals billed only at Levels 4 or 5.

The AMA’s Hoven warned that the data could reflect errors or attribute high-priced visits to one doctor when the services were actually provided by another. Further, she said, because a growing number of seniors have multiple chronic conditions and complex medical histories, more Level 4 or 5 office visits may be justified.

But other health industry leaders called the billing patterns identified by our analysis troubling.

“I can’t see a situation where every visit would be a Level 5, especially on an established patient,” said Cyndee Weston, executive director of the American Medical Billing Association, an industry trade group. “I was trying to talk myself into it, but I just can’t see it.”

She said such providers “would be ripe for audit,” because they are outliers.

Medicare has long known that office visits are susceptible to fraud and what’s known as “upcoding,” or billing for a more expensive service than was actually performed.

May 2012 report from the U.S. Department of Health and Human Services’ inspector general found that doctors are choosing higher codes more often for evaluation and management services, the broad category that includes office visits. The proportion of Level 4 visits by established patients increased by 15 percentage points from 2001 to 2010, while Level 3 visits dropped by eight points.

The HHS inspector general recommended that Medicare educate doctors, ask its contractors to review E&M billings, and conduct detailed reviews of physicians who consistently bill for higher-level visits. CMS administrator Marilyn Tavenner agreed with the first two recommendations but only committed the agency to reviewing a small number of the highest billers.

She noted that the return on investment to check billings for visits wasn’t great. The average error cost Medicare $43, but the program paid $30 to $55 to review each claim.

Using a sample of Medicare data, non-profit investigative group the Center for Public Integrity found a similar trend in upcoding office and emergency room visits across the country in an analysis it published in September 2012. And a Medicare report from 2013 estimated that established patient visits had a seven percent improper payment rate, accounting for approximately $965 million in 2012.

“That’s real money coming out of the Treasury,” the Urban Institute’s Berenson said. “Some doctors are robbing the commons for themselves.”

By looking at provider-level data, patients can evaluate their doctors’ billing patterns. The providers flagged by ProPublica stand out from others in their specialties and states. Some were senior doctors at prominent teaching hospitals who may disproportionately care for complex cases; most were not.

Agomuoh was one of 790 Michigan obstetrician-gynecologists who billed Medicare for established patient visits in 2012. Together, these doctors billed for about 61,000 office visits, of which seven percent were classified as Level 5. By contrast, 97 percent of Agomuoh’s office visits were at the highest level. His Level 5 visits accounted for 35 percent of those for all ob-gyns in Michigan.

In an interview, Agomuoh said he does not believe the data is accurate, even though Medicare says it is. Agomuoh also said he takes on tough patients other providers won’t see in the impoverished community of Hamtramck, Michigan, outside Detroit.

“Most of these patients have been rejected by other doctors,” he said. “I’m probably the only one taking care of them.”

But Agomuoh’s Medicare billings were unusual in other ways, too, ProPublica’s analysis showed.

The program paid for wheezing evaluations for every one of his patients in 2012, at $50 a pop. On average, each of his patients was checked for wheezing eight times. Almost all of his patients also received an average of seven ultrasounds of arteries in the legs (at $149 per test) and seven ultrasounds of arteries in the arms (at $144 per test). Most of his peers rarely, if ever, billed for these services. All told, Medicare paid Agomuoh $769,000 in 2012.

Agomuoh has a long history of discipline against his medical licenses and has been sanctioned for negligence, making false statements, and failing to pay child support and lying about it. He has surrendered his license in New York, agreed not to renew his license in Connecticut, withdrawn his application for a license in Ohio, and was once on probation in Michigan.

Agomuoh, who is running for governor of a state in Nigeria, where he was born, said his billings reflect that many of his patients have asthma, chronic obstructive pulmonary disease, and drug addictions. He initially said a reporter could visit his office but then changed his mind a day later, referring further questions to his lawyer, Fred Freeman.

“Why are you bothering him?” Freeman asked. “You’re not being fair to him at all. He has nothing to say to you.”

Medicare declined to answer questions about Agomuoh, or about other individual practitioners, and there’s no indication that program officials have challenged his billings. Medicare officials have said that their data may not take into account money collected by a provider and subsequently returned to CMS, or payments that “may have been withheld after claims were already processed but prior to release to the provider.”

Medicare did question the billing practices of Im, the doctor who coded 100 percent of his visits as Level 5. Im runs Exceptional Urgent Care in The Villages, a huge retirement community in central Florida, and said that because of his training as an emergency room physician, his center attracts sicker patients than others do.

He said that after being contacted by Medicare officials last year, he took “voluntary tutoring and counseling” and now estimates that around 90 percent of his office visits are Level 5.

“Yes it was inaccurate in 2012,” he conceded, blaming his coding problems, in part, on Medicare’s lack of billing categories tailored to urgent care. “Medicare gave us a call. 2013 is going to be a lot more accurate.”

Experts, however, said that it was implausible that an urgent care doctor would never see patients with minor ailments. Other urgent care centers in the region, including some run by emergency specialists, have lower proportions of Level 5 visits, ProPublica’s analysis showed.

“Bring in the logic police,” said Shelley C. Safian, who teaches medical billing and has written textbooks on the topic. “Even an emergency room in a hospital, not everybody is a Level 5.”

Im earned $237,600 from the government for his Level 5 visits in 2012, plus patient co-pays. Im is still a Medicare provider in good standing, according to the program’s Physician Compare website, and Medicare declined to respond to questions about him.

Farhoomand offered a similar explanation to Im’s for why his patient visits were predominantly coded at the top level. All told, the San Diego-area internist billed Medicare for more than 2,100 Level 5 visits, one of the highest tallies in the nation.

“I have a predominantly geriatric population, and I do mostly chronic critical illness, so all of my patients have, like, multi-organ failure, heart failure, diabetes with multiple complications, etc. etc.,” he said. “I’m savvy enough that I handle most of their issues myself, and I use specialists only for procedures and such things.”

Farhoomand is facing a 2013 accusation by the California medical board of gross negligence in his prescribing of controlled substances, a charge he denies.

“No good deed goes unpunished,” he said. “I wind up managing most of their chronic pain.”

He said he is in talks with the board to settle the accusation.

Experts say there are plenty of flaws with the way Medicare reimburses doctors. The program pays a premium for hands-on procedures, such as inserting a pacemaker, but undervalues the decision-making at office visits to sort out the cause of a complaint and the proper treatment, some say.

Dr. Christine Sinsky, a Dubuque, Iowa, internist has shadowed more than 50 physician practices to assess the way they are organized and has written about the topic. She said she worries that as Medicare imposes more rules and requirements, the focus is shifting away from patients’ needs and toward checking boxes on electronic health records. These systems are designed to keep better track of doctors’ services but have been linked to upcoding.

“Physicians are very afraid of being an outlier,” Sinsky said. “I have a lot of compassion for physicians who are struggling with the billing rubric, because it is sometimes a force pushing us away from what we know is best for our patients.”

Indeed, some health professionals blamed billing issues on electronic health systems. Arizona optometrist Serge Wright was surprised to learn that 959 of his 2012 office visits were coded as Level 5—and that he’d charged the top rate more than all the other optometrists in the state put together.

“Wow, that sounds distorted,” he said.

Wright speculated that the coding could reflect a switch to a new electronic medical record system a couple of years ago.

“I don’t think I ever used a 99215 [Level 5 visit code]” until then, he said, noting that the new system is supposed to check whether enough documentation has been entered to justify each charge. “In the past, without the electronic records, it took more time to keep track of all of the elements of an exam to code it. I think everyone was undercoding at that point, myself included.”


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Age old debate-what is considered further workup – AAPC Medical …

Ok all my coder counterparts our there… I have a question which I am sure will get many different opinions..

What actually is considered “additional work-up” ?

We are auditing for a large practice and they have the capability to do there “in-house” ua’s , rapid streps and flu tests, etc. They also perform x-rays. I have my thought process with these tests that are ordered and completed, but being the information nerd that I am, I have been reading many different articles on the topic. So, what is everyone’s opinion or take on what is considered additional work-up?

Thanks.. I always appreciate my counterparts opinions.

JMIR–Monitoring of Internet Forums to Evaluate Reactions to the …

This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance 

Advertisement: Preregister now for the Medicine 2.0 Congress

Original Paper

Monitoring of Internet Forums to Evaluate Reactions to the Introduction of Reformulated OxyContin to Deter Abuse

Emily C McNaughton1, MPH; Paul M Coplan2,3, ScD; Ryan A Black4, PhD; Sarah E Weber1, BS; Howard D Chilcoat2,5, ScD; Stephen F Butler1, PhD

1Inflexxion, Inc., Newton, MA, United States
2Purdue Pharma L.P., Stamford, CT, United States
3Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
4Center for Psychological Studies, Nova Southeastern University, Fort Lauderdale, FL, United States
5Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States

Corresponding Author:

Emily C McNaughton, MPH

Inflexxion, Inc.
320 Needham Street, Suite 100
Newton, MA, 02464
United States
Phone: 1 617 614 6028 ext 256
Fax: 1 617 332 1820


Background: Reformulating opioid analgesics to deter abuse is one approach toward improving their benefit-risk balance. To assess sentiment and attempts to defeat these products among difficult-to-reach populations of prescription drug abusers, evaluation of posts on Internet forums regarding reformulated products may be useful. A reformulated version of OxyContin (extended-release oxycodone) with physicochemical properties to deter abuse presented an opportunity to evaluate posts about the reformulation in online discussions.
Objective: The objective of this study was to use messages on Internet forums to evaluate reactions to the introduction of reformulated OxyContin and to identify methods aimed to defeat the abuse-deterrent properties of the product.
Methods: Posts collected from 7 forums between January 1, 2008 and September 30, 2013 were evaluated before and after the introduction of reformulated OxyContin on August 9, 2010. A quantitative evaluation of discussion levels across the study period and a qualitative coding of post content for OxyContin and 2 comparators for the 26 month period before and after OxyContin reformulation were conducted. Product endorsement was estimated for each product before and after reformulation as the ratio of endorsing-to-discouraging posts (ERo). Post-to-preintroduction period changes in ERos (ie, ratio of ERos) for each product were also calculated. Additionally, post content related to recipes for defeating reformulated OxyContin were evaluated from August 9, 2010 through September 2013.
Results: Over the study period, 45,936 posts related to OxyContin, 18,685 to Vicodin (hydrocodone), and 23,863 to Dilaudid (hydromorphone) were identified. The proportion of OxyContin-related posts fluctuated between 6.35 and 8.25 posts per 1000 posts before the reformulation, increased to 10.76 in Q3 2010 when reformulated OxyContin was introduced, and decreased from 9.14 in Q4 2010 to 3.46 in Q3 2013 in the period following the reformulation. The sentiment profile for OxyContin changed following reformulation; the post-to-preintroduction change in the ERo indicated reformulated OxyContin was discouraged significantly more than the original formulation (ratio of ERos=0.43, P<.001). A total of 37 recipes for circumventing the abuse-deterrent characteristics of reformulated OxyContin were observed; 32 were deemed feasible (ie, able to abuse). The frequency of posts reporting abuse of reformulated OxyContin via these recipes was low and decreased over time. Among the 5677 posts mentioning reformulated OxyContin, 825 posts discussed recipes and 498 reported abuse of reformulated OxyContin by such recipes (41 reported injecting and 128 reported snorting).
Conclusions: After introduction of physicochemical properties to deter abuse, changes in discussion of OxyContin on forums occurred reflected by a reduction in discussion levels and endorsing content. Despite discussion of recipes, there is a relatively small proportion of reported abuse of reformulated OxyContin via recipes, particularly by injecting or snorting routes. Analysis of Internet discussion is a valuable tool for monitoring the impact of abuse-deterrent formulations.

(J Med Internet Res 2014;16(5):e119)


Internet; opioid analgesic; drug abuse; prescription drug; OxyContin; epidemiology; surveillance; social media; qualitative research

Prescription opioid analgesics are an important component of pain management. Misuse and abuse of these medications, however, have created a serious and growing public health problem [1]. The balance between providing access to and prescribing these medications for patients with chronic pain while minimizing their diversion and abuse remains a significant challenge for all stakeholders, including prescribers, pharmaceutical manufacturers, and the Food and Drug Administration [2,3]. One important step toward the goal of creating safer opioid analgesics has been the development of opioid formulations designed to deter abuse [4-6]. These formulations are commonly referred to as abuse-deterrent formulations (ADFs) [7] or tamper-resistant formulations (TRFs). The science of deterring abuse via these formulations is new, and both the formulation technologies and the analytical, clinical, epidemiological, and statistical methodology for evaluating those technologies are rapidly evolving.

Most abuse-deterrent technologies developed to date are designed to make product manipulation more difficult or to make abuse of the manipulated product less attractive or rewarding. Although in vitro and clinical studies indicate the efficacy of these technologies, postmarketing data are needed to evaluate their effectiveness. One of the early formulations intended to reduce abuse was a reformulated version of extended-release oxycodone (reformulated OxyContin, Purdue Pharma, Stamford, CT, USA), which was introduced to the market in August 2010. This product has physicochemical resistance to crushing and dissolution intended to present obstacles to abuse by nonoral routes of administration (ROA) (eg, injecting, snorting). The launch of reformulated OxyContin provided a nationwide experiment to evaluate the impact of a product intended to reduce tampering in the real world [8,9]. To date, evidence from individuals evaluated for treatment triage suggests that reformulated OxyContin results in lower rates of abuse through nonoral abuse and abuse via any ROA [8] compared to historical rates for the original formulation of OxyContin. These findings, as well as others [10,11] that suggest reformulated OxyContin inhibits manipulation and abuse, are based on reports by abusers to some authority (eg, researcher, treatment provider, poison control center). The question arises as to the reaction to reformulated OxyContin of individuals who abuse prescription opioids and are not reporting abuse to researchers or other authorities. It is of further interest to monitor and describe the extent to which individuals are engaging in efforts to defeat the tamper-resistant properties of reformulated OxyContin and whether such efforts were deemed feasible.

Introduction of reformulated OxyContin presents an opportunity to determine the utility of monitoring Internet data to evaluate reactions to this formulation among a difficult-to-reach population of prescription drug abusers who are not generally in contact with some authority [12]. Because these Internet data reflect uninhibited peer-to-peer communications, they may be a useful source for monitoring and tracking efforts to defeat the abuse-deterrent properties of the product for illicit use. It is generally believed that these efforts will take the form of “recipes” that will be disseminated via the Internet [13-15]. Furthermore, it is anticipated that the feasibility and utility of a recipe will be evaluated by abusers online and that practical tampering methods will be disseminated and perpetuated through postings on websites dedicated to recreational abuse of drugs [15]. Based on this scenario, public health stakeholders are increasingly concerned about monitoring discussions around extraction techniques that emerge on the Internet and tracking the dissemination of these methods [2].

Although public Internet forums can be monitored unobtrusively and might reveal ways in which prescription drugs are being misused [16], there has been little published to date on how to collect, analyze, and understand the messages within the large volume of posts available from online recreational drug abuse communities. Early studies [17,18] that examined the feasibility of systematic Internet surveillance of discussion of prescription opioid products indicated that Internet posts can be reliably coded for sentiment (eg, endorsing vs discouraging abuse) and that both the amount of discussion and sentiment differentiated products [18]. In subsequent work, McNaughton et al [12] developed a metric, referred to as the endorsement ratio (ERo), to evaluate and quantify the overall sentiment expressed by a large number of opioid abusers who post online about prescription opioid products.

In the present work, we sought to understand how drug abusers reacted to the introduction of an intended tamper-resistant prescription opioid product to the market. We examined data from abusers who participated in Internet message boards to evaluate discussion of OxyContin before and after introduction of the reformulation. Specifically, we investigated these questions: (1) did the level of Internet discussion related to OxyContin change quantitatively over time following introduction of the reformulated version of the product, (2) within the OxyContin-specific discussion that did occur, was there a shift in the sentiment expressed by abusers who posted on these websites following the introduction of reformulated OxyContin, and (3) given concerns about efforts to generate and disseminate tampering methods intended to defeat the properties of reformulated OxyContin for use by unintended ROAs, could Internet discussion of such recipes be defined, identified, and monitored?

Study Overview

The study aimed to evaluate the potential effect the introduction of the reformulation of OxyContin had on discussion within Internet-based recreational drug abuse message boards. Over the pre-post reformulated OxyContin timeframe, we conducted (1) a quantitative evaluation of message board discussion for OxyContin and comparators to capture the relative levels of discussion and any changes during the pre-post time period, (2) a qualitative coding of Internet post content and estimation of endorsement for OxyContin and comparators to determine any changes in the sentiment in favor of each medication for abuse purposes from pre to post OxyContin reformulation, and (3) in the period following the introduction of OxyContin, an evaluation of Internet post content related to tampering methods for defeating the abuse-deterrent properties of reformulated OxyContin. All research activities conducted for this study were exempt from Institutional Review Board review as determined by the New England Institutional Review Board.

For the quantitative evaluation of discussion levels and content analysis/estimation of endorsement, Vicodin (hydrocodone) and Dilaudid (hydromorphone) were selected as comparators. These comparators represented a widely available and highly abused prescription opioid (Vicodin) and a high-potency opioid analgesic that is highly desirable for abuse (Dilaudid) [19]. In order to make appropriate comparison to the target product (OxyContin), qualitative coding and analysis was restricted to discussion of the proprietary products Vicodin and Dilaudid only and did not include generic references to hydrocodone, hydromorphone, and other proprietary products within the opioid compounds (eg, Lortab for hydrocodone and Exalgo for hydromorphone).

Data Source

The study sample consisted of Internet posts (ie, messages) copied from 7 publically accessible message boards that represent a population of drug abusers and their online communications regarding both illicit and prescription drugs. The websites were chosen based upon predefined criteria as described in McNaughton et al [12]. All posts written between January 1, 2008 and September 30, 2013 (N=6,891,514) were archived in a database for further sampling and analysis. No personal identifiable information related to the author was retained.

Quantitative Evaluation of Message Board Discussion

From the database of saved Internet posts, all messages related to OxyContin (both original and reformulated versions of the product), Vicodin, and Dilaudid written between January 1, 2008 and September 30, 2013 (ie, Q1 2008 through Q3 2013) were identified through the use of standardized queries. These queries contained text-matching criteria that included common misspellings, slang, and wildcard characters as well as exclusion criteria to capture as many relevant posts as possible while minimizing the number of false positives (ie, posts returned by the query that are not actually related to the target product) selected. It should be noted, however, that false positives could not be completely eliminated from the text-matching query results without manual review, which was not conducted for this analysis because of the magnitude of posts involved. The rate of discussion related to each product was then calculated as the number of product-specific posts identified per 1000 posts saved within the database per quarter.

Formal Content Analysis and Estimation of Endorsement

A formal content analysis was conducted on random samples of Internet posts related to OxyContin, Vicodin, and Dilaudid during the 26-month period before (preintroduction period=June 1, 2008 through July 30, 2010) and the 26-month period after the introduction of reformulated OxyContin (postintroduction period=August 1, 2010 through September 30, 2012) and identified through the use of the standardized queries. For this analysis, posts retained for coding in the preintroduction period pertained to the original formulation of OxyContin, whereas posts sampled and retained in the postintroduction period related specifically to reformulated OxyContin. Because the design involved comparison of discussion of original OxyContin in the preintroduction period and reformulated OxyContin in the postintroduction period, discussion of original OxyContin in the postintroduction period was not examined for this study. Using systematic query searches, product-specific Internet posts were randomly sampled from the archive. All coding was conducted as part of a larger dynamic postmarketing surveillance program, involving rolling sampling and content analysis of posts (ie, multiple waves of sampling throughout the study period). Power analyses to determine the sample size needed to detect changes were calculated periodically throughout surveillance and changed over time resulting in somewhat different sample sizes in the preintroduction and postintroduction periods for this evaluation.

The coding procedure and assessment of intercoder agreement used in this study is described in detail in McNaughton et al [12]. Briefly, posts were reviewed by trained coders and categorized as either abuse-related or non-abuse-related, and false positives were removed and replaced. A false positive is a query-selected post that upon manual review did not pertain to the specified prescription opioid product. Within the sample of abuse-related posts, product-specific content was further coded as endorsing, discouraging, mixed, or unclear (ie, the sentiment was assigned) (Figure 1). When there was disagreement between coders, the post content was discussed and reviewed by an independent lead coder for a final rating and to achieve a final set of codes for analysis. To assess reliability of the coding, 20% of all posts were coded by 2 coders who were blinded to which posts were coded by both coders and which were coded independently. Interrater agreement (kappa) was then calculated on the 20% overlapping sample to determine if an acceptable level of coder reliability was achieved [20].

A mixed effects multinomial logistic regression was employed to model the probability of observing each of the 4 types of abuse-related Internet posts (endorsing, discouraging, mixed, and unclear) per product. The fixed effects included a product indicator (1=product A, 2=product B, etc), time indicator (1=preintroduction period, 2=postintroduction period) and product×time interaction. An author random effect was incorporated in the model to account for correlation among messages posted by the same author. The GLIMMIX procedure in SAS 9.3 (SAS Institute, Inc, Cary, NC, USA) was used to fit the model, producing the following statistics of interest:

  1. Probability of observing each type of abuse-related post (endorsing, discouraging, mixed, and unclear) per product in the period before and after the introduction of reformulated OxyContin.
  2. Endorsement ratio (ERo) for each product in the period before and after the reformulation of OxyContin. The ERo provides a relative estimate of the extent to which a product was being endorsed during each time period by calculating a ratio of probabilities (eg, probability of endorsing product A in the postintroduction period divided by probability of discouraging product A in the postintroduction period), commonly referred to as a relative risk [12].
  3. Post-to-preintroduction change in the ERo was estimated by calculating the ratio of ERos (eg, ERo of product A in the postintroduction period divided by ERo of product A in the preintroduction period), commonly referred to as a relative risk ratio.
  4. Within-author correlation as estimated by intraclass correlation coefficients derived from the variance components [21].