a new patient visits the internal medicine clinic

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Outpatient visit trends for internal medicine ambulatory care sensitive conditions after the COVID-19 pandemic: a time-series analysis

Ciara pendrith.

1 Temerty Faculty of Medicine, University of Toronto, Toronto, ON Canada

2 Women’s College Hospital Institute for Health System Solutions & Virtual Care, 76 Grenville Street, Toronto, Ontario M5S 1B3 Canada

Dhruv Nayyar

3 Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada

Tara O’Brien

4 Women’s College Hospital, Toronto, ON Canada

Owen D. Lyons

Payal agarwal.

5 Department of Family and Community Medicine, University of Toronto, Toronto, ON Canada

Danielle Martin

R. sacha bhatia.

6 Peter Munk Cardiac Centre, University Health Network, Toronto, ON Canada

Geetha Mukerji

Associated data.

Available on request from Dr. Geetha Mukerji. There were no public or administrative databases used for this project. The sole data source for this project was the Electronic Medical Record at Women’s College Hospital, Epic. The Women’s College Hospital Research Ethics Board approved access.

The COVID-19 pandemic led to a dramatic shift in the delivery of outpatient medicine with reduced in-person visits and a transition to predominantly virtual visits. We sought to understand trends in visit patterns for ambulatory care sensitive conditions (ACSCs) commonly seen in internal medicine clinics.

We included adult outpatients seen for an ACSC between March 15th, 2017 and March 14th, 2021 at a single-centre in Ontario, Canada. Monthly visits were assessed by visit type (new consultation, follow-up), diagnosis, and clinic. Time series analyses compared visit volumes pre- and post-pandemic. Proportion of virtual visits were compared before and during the pandemic. Patient and visit factors were compared between in-person and virtual visits.

8274 patients with 34,021 visits were included. Monthly visits increased by 15% during the pandemic ( p  <  0.0001). New consultations decreased by 10% ( p  = 0.0053) but follow-up visits increased by 21% (p <  0.0001). Monthly heart failure visits increased by 43% ( p  <  0.0001) whereas atrial fibrillation visits decreased. Pre- pandemic, < 1% of visits were virtual compared to 82% during the pandemic (p <  0.0001). Less than half of heart failure visits were virtual whereas > 95% of diabetes visits were virtual.

Conclusions

We found a significant increase in overall visits to internal medicine clinics driven by increased volumes of follow-up visits, which more than offset decreased new consultations. There was variability in visit trends and uptake of virtual care by visit diagnosis, which may indicate challenges with delivery of virtual care for certain conditions.

The COVID-19 pandemic led to rapid changes in the delivery of healthcare services to meet patient needs throughout the pandemic while reducing the risk of exposure to SARS-CoV-2. Among other adaptations, healthcare systems rapidly transitioned from traditional in-person office-based visits to predominantly virtual care [ 1 – 4 ].

Within Ontario, a Canadian province with a population of 14,500,000 and universal health insurance, adoption of virtual care was low before the pandemic [ 5 , 6 ]. Barriers included challenges with reimbursement, policy issues, and limited access to technology [ 1 , 7 – 9 ]. Within Ontario specifically, virtual delivery of physician services were previously only covered by the Ontario Health Insurance Plan if delivered from a government-approved facility using a government-approved telemedicine platform [ 3 , 5 ]. 3,5 After the declaration of COVID-19 as a global pandemic, new temporary billing codes were issued on March 14th, 2020, which allowed physicians to be renumerated for outpatient visits conducted via telephone or videoconference at the same rate as an in-person visit which facilitated the widespread adoption of virtual care [ 3 , 10 ]. While previous studies have examined changes in visit volumes and uptake of virtual care during the pandemic, these have generally been limited to primary care [ 4 , 5 , 10 ] or narrow scopes of care [ 11 , 12 ].

To our knowledge, no study to date has assessed the COVID-19 pandemic’s impact on visit trends and utilization of virtual care across multiple internal medicine ambulatory care sensitive conditions (ACSCs). ACSCs are health conditions where high quality and timely care may prevent acute exacerbation, sequelae of disease, and emergency department visits or hospitalizations [ 13 ]. By assessing changes in outpatient visits for several ACSCs across a single institution, we can understand the relative impact of the pandemic and virtual care on different conditions. These findings may highlight areas where virtual care is better suited than others. Additionally, understanding care patterns for multiple ACSCs may help organizations plan future allocation of resources within an evolving healthcare environment in the ongoing pandemic with the goals of preventing clinical deterioration and avoidable inpatient visits.

Our objectives were to understand how the onset of the COVID-19 pandemic and introduction of virtual care billing codes impacted patterns of care and uptake of virtual care for outpatients with common internal medicine ACSCs at a large academic ambulatory hospital in Ontario, Canada. Furthermore, we sought to understand patient and visit factors associated with utilization of virtual care.

Design, setting and participants

We conducted a repeated cross-sectional study of all patients seen in internal medicine clinics for an ACSC at Women’s College Hospital (WCH) in Toronto, Ontario between September 15th, 2017 and March 14th, 2021. WCH is an ambulatory care hospital affiliated with the University of Toronto.

Internal medicine clinics included were the following: General Internal Medicine (GIM) clinic; Acute Ambulatory Care Unit, which is a short stay (< 24 h) unit providing urgent assessment, and treatment of internal medicine conditions; Cardiology clinic; Atrial Fibrillation clinic; General Endocrinology clinic; Diabetes clinic; and Respirology clinic. ACSCs included in this study were hypertension, congestive heart failure, coronary artery disease, atrial fibrillation, diabetes mellitus, asthma, and chronic obstructive pulmonary disease [ 13 ].

Patient visits meeting the following criteria were included: 1) main visit diagnosis was an ACSC to an internal medicine clinic; and 2) visit occurred during the study time period. Visits were included if the healthcare provider was a physician, nurse practitioner, or nurse. Both new consultations and follow-up visits were included. Patient encounters were excluded if the encounter was for a procedure with no corresponding office or virtual care visit. Multiple same day visits to different providers were excluded if the encounter ID was the same (e.g. seen by physician and nurse for the same encounter).

The study period is divided into pre-pandemic and pandemic periods which are defined as September 15th, 2017-March 14th, 2020 and March 15th, 2020-March 14th, 2021, respectively. The study period was selected to include two and a half years of data before and one year after the introduction of temporary virtual care billing codes on March 14th, 2020 to allow sufficient data points to adjust for pre-pandemic trends in the time series analysis.

Data sources

Data for this study were collected from the hospital’s electronic health record EPIC. Data on visit information and patient characteristics were extracted electronically using a data query, and a manual chart review was performed for any missing or unknown variables. Video visits were delivered through Zoom videoconferencing technology [ 14 ] integrated through the electronic medical record patient portal myHealthRecord [ 15 ].

Baseline characteristics

For each included visit we extracted the visit date, main diagnosis using the diagnostic code, clinic, visit type (new consultation, follow-up visit), and visit modality (in-person, or virtual including telephone and videoconference visits). The following baseline patient characteristics were captured at the time of each visit: age, sex, and whether the patient had an email registered.

Our primary outcome was change in average monthly visits after the onset of the pandemic and introduction of virtual care billing codes. We assessed changes in visit volumes overall, as well as by visit type to understand changes in number of new consultations and follow-up visits. Visit volumes by visit diagnoses were compared to understand differences in the types of patient conditions being seen. Additionally, we assessed the proportion of virtual visits in the pre-pandemic and pandemic periods. Secondary outcomes included patient and visit factors associated with virtual care vs. in-person visits in the pandemic period.

Statistical analyses

Baseline characteristics of patients and visits were compared before and during the pandemic using Chi-square tests and t-tests. Average monthly visit numbers were compared during the pre-pandemic and pandemic periods using two-sample t-tests and the change in monthly visits was calculated as both the mean and relative difference. To understand differences in pandemic visit volumes compared with historical data, time series analyses were performed using interventional Autoregressive Integrated Moving Average (ARIMA) models to examine the statistical significance of the change in visit volumes from pre-pandemic to during. Models were run for total visits, new consultations, follow-ups, and each visit diagnosis. Interventional ARIMA modelling is commonly used to analyze the effect of change (e.g. major event or intervention) in a time-series analysis [ 16 ]. A step function was applied to the model to characterize the start of the pandemic. Secondary outcomes of factors associated with virtual visits in the pandemic period were assessed using Chi-square tests and two-sample t-tests. P -values < 0.05 were considered statistically significant. All statistical analyses were completed using Microsoft Excel and SAS version 9.4.

Ethics approval was received from the Research Ethics Board at Women’s College Hospital (REB approval #: 2019–0191-E).

Study participants and visits

A total of 8274 unique patients with 34,021 outpatient visits were included in the study. Most patients had multiple visits during the study time period, with 28.2% having one visit, 16.9% having two visits, and 54.9% having three or more visits. Characteristics of patients and their visits are included in Table  1 . The mean age was 58.1 years and 61.4% of visits were delivered to female patients; 52.3% of patients had an email registered. There were no differences in patient demographics in the pre-pandemic and pandemic periods.

Baseline characteristics of patients and visits in the pre-pandemic and pandemic periods

AFib atrial fibrillation, CAD coronary artery disease, CHF congestive heart failure, HTN hypertension, DM diabetes mellitus, COPD chronic obstructive pulmonary disorder, AACU Acute Ambulatory Care Unit, GIM general internal medicine

There were 23,279 and 10,742 visits in the pre-pandemic and pandemic periods, respectively. The proportion of new consultation visits decreased from 17.2% pre-pandemic to 13.4% during the pandemic ( p  <  0.0001). Pre-pandemic, 99.9% of visits were conducted in-person with only 18 virtual visits, all of which were telephone visits for diabetes patients seen in the General Endocrinology and Diabetes clinics. In the pandemic period, the proportion of in-person visits decreased to 18.1% ( p  <  0.0001) with the majority of virtual visits being conducted over telephone (95.3%) and a minority by videoconference (4.7%).

Visit trends

Figure  1 displays monthly overall visits, new consultations, and follow-up visits across the study period. Results from the time series analysis are shown in Table  2 . Average monthly overall visits decreased by 15% between the pre-pandemic and pandemic periods (776 vs. 895), new consultations decreased by 10% (134 vs. 120), and follow-ups increased by 21% (642 vs. 776). The changes in visit volumes were significant for all types of visits: overall visits ( p  <  0.0001), new consultations (0.0053), and follow-ups (p <  0.0001).

Average monthly overall, new consultation, and follow-up visits. The black vertical line represents the start of the COVID-19 pandemic

Average monthly visits in the pre-pandemic and pandemic periods

AFib atrial fibrillation, CAD coronary artery disease, CHF congestive heart failure, HTN hypertension, DM diabetes mellitus, COPD chronic obstructive pulmonary disorder

* p -values are derived from ARIMA models

Average monthly visits by visit diagnosis are displayed in Fig.  2 . Monthly visits for atrial fibrillation decreased by 18% (60 vs. 49) while congestive heart failure visits increased by 43% (44 vs. 63). The change in visit volumes was significant for both atrial fibrillation ( p  = 0.013) and congestive heart failure ( p  <  0.0001). There were increases in monthly visits for hypertension (20%), diabetes (16%), and asthma (23%). The increases for these conditions were significant in the ARIMA model. There was no significant difference in monthly visits for coronary artery disease or chronic obstructive pulmonary disorder.

Average monthly visits by visit diagnosis. The black vertical line represents the onset of the COVID-19 pandemic. AFib = atrial fibrillation, CAD = coronary artery disease, COPD = chronic obstructive pulmonary disorder, DM = diabetes mellitus, HTN = hypertension

Figure  3 shows the proportion of visits by visit modality in the pandemic period. The second month of the pandemic (April 2020) saw the lowest monthly proportion of in-person visits at 7% after which in-person visits gradually increased until September 2020, when 28% of monthly visits were in-person. After September 2020, the proportion of in-person visits decreased to 18–23% per month.

Visit modality proportions by study month in the pandemic period

Factors associated with virtual visits in the pandemic period

Table  3 shows patient and visit factors by visit modality after the onset of the pandemic. Patients seen virtually during the pandemic were significantly younger than those seen in-person (mean age 64.9 seen in-person vs. 56.6 seen virtually, p  <  0.0001). Male patients were more likely to be seen in-person with 20.2% of visits by male patients being in-person compared to 16.7% of visits by female patients ( p  <  0.0001). Patients with an email registered with the hospital were more likely to be seen virtually than those without an email on file (85.7% vs. 76.7%, p <  0.0001).

Patient and visit characteristics by visit modality in the pandemic period

New consultations were more likely to be seen in-person (33.5%) than follow-up visits (15.7%, p <  0.0001). There were large differences in the proportion of virtual visits by clinic location in the pandemic period. Over 85% of visits to the Diabetes, General Endocrinology, General Internal Medicine, and Cardiology clinics were conducted virtually after the pandemic onset. In contrast, visits to the Acute Ambulatory Care Unit and Atrial Fibrillation clinics were mostly in-person (10.1 and 27.0% virtual visits, respectively). Amongst congestive heart failure visits, 48.1% were seen virtually. Greater than 65% of visits for all other diagnoses were seen virtually. The most common conditions seen virtually were coronary artery disease (86.6%) and diabetes (95.5%).

In this analysis of outpatient visits to internal medicine clinics for ACSCs at a large ambulatory hospital in Ontario, Canada, we found a dramatic shift towards virtual care after the onset of the COVID-19 pandemic, as well as several changes in visit trends for overall visits and by specific visit diagnoses. While new consultations decreased by 10% during the pandemic, this was offset by an increase in follow-up visits leading to an overall 15% increase in visits. Our results showed a transition from primarily in-person visits pre-pandemic to predominantly virtual care during the pandemic. Importantly, there were several factors associated with visit modality in the pandemic period, with patients who were older, men, and without a registered email account more likely to be seen in-person than virtually. There were also significant differences in uptake of virtual care by clinic and visit diagnosis, with less than half of heart failure visits being seen virtually, in contrast to diabetes care which was nearly entirely virtual.

The dramatic shift towards virtual care during the COVID-19 pandemic has been consistently shown in other studies across a number of jurisdictions, which is consistent with our findings [ 1 – 5 , 10 – 12 , 17 – 19 ]. A study of visit trends to Veterans Affairs clinics in the United States during the first 10 weeks of the pandemic showed a decrease in in-person visits by 56% which was partially offset by an increase in telephone and video visits, but overall visits still decreased by 30% [ 1 ]. Similarly, other studies from the US and Canada have shown an overall decline in visit numbers after the pandemic [ 10 , 20 , 21 ]. These findings are in contrast to our findings of a significant increase in patient volumes during the pandemic after accounting for prior trends, which was driven by more frequent follow-up visits. The reasons for these differences in findings are unclear and may be due to our study having a longer follow-up period allowing for a correction in visits after an initial drop with the start of the pandemic or due to differences in populations and conditions studied.

A prior study demonstrated that new consultations were postponed while follow-up visits shifted to primarily telemedicine during the pandemic [ 22 ], which is consistent with our findings, but the literature on patterns in internal medicine is lacking. We do not have data on the reasons leading to decreased new consultations and increased follow-up visits; however, there are several potential factors. Decreased visits in primary care during the pandemic may have led to decreased referrals as demonstrated in Ontario [ 10 ] and other jurisdictions during the first wave of the pandemic [ 23 – 25 ]. Other reported barriers, such as patient concerns about acquiring COVID-19 leading to delay seeking care [ 26 ] and decreased access to care [ 27 ] may have also impacted new visits. Regarding increased follow-up visits, telephone visits may have facilitated easier access to follow-up visits with a prior study showing that virtual follow-up visits were more efficient [ 28 , 29 ]. Similarly, both patients and providers have reported feeling telemedicine visits worked best for follow-up visits [ 30 ]. Furthermore, with the introduction of billing codes for telephone visits, physicians may be booking more follow-up telephone visits for brief calls that would have previously occurred but not been remunerated. Increased follow-up visits may be due to physicians wanting closer follow-up because they are unable to examine their patients, patients seeking specialist care rather than going to emergency departments for exacerbations of their chronic illness, decreased patient access to primary care [ 10 ] leading to more frequent telephone visits with specialists, or physicians feeling more comfortable asking patients for frequent follow-ups because of the convenience of telemedicine [ 29 ]. Finally, delays in access to tests and procedures during the pandemic [ 27 ] may have led to patients needing to rebook earlier follow-up after completing delayed investigations.

To our knowledge, our study is the first to examine how visits for a range of common internal medicine clinic conditions changed during the pandemic. We found variable uptake of virtual care across different clinics and conditions. Patient visits for CHF, hypertension, and asthma each increased by 20%, whereas atrial fibrillation visits decreased by nearly 20%. We cannot discern the reasons for the variability by condition but there are several potential patient, provider, and system factors that may contribute. Our finding that patients who were older were less likely to use virtual care is consistent with past studies, [ 12 , 21 , 31 , 32 ] as well as those showing that older patients are less likely to have internet and are slower to adopt technology [ 33 – 35 ]. Also, sicker or older patients may require more frequent follow-up. For example, CHF patients who are at risk of exacerbation may need closer follow-up to remotely monitor for signs of exacerbation with reduced in-person visits for volume status exams. Similarly, hypertension may lend itself easily to virtual follow-up if patients monitor their blood pressure at home allowing for quick telephone visits to titrate medications [ 29 ]. Female patients were more likely to be seen virtually in our study, although findings from other studies on sex differences in virtual care uptake have been mixed [ 10 , 21 , 31 ]. In terms of provider factors, a prior study showed that physicians viewed virtual care as optimally suited for managing conditions that primarily involved counselling and were less reliant on physical exams, and in particular, that hypertension and diabetes were easily managed virtually [ 29 ]. Other provider factors that may contribute to variability include familiarity or lack thereof with virtual mediums or supplier-induced demand. Finally, system factors likely contributed to decreased atrial fibrillation visits because of decreased clinic hours and fewer presentations to the emergency department, which is the primary referral mechanism to this clinic.

There are several limitations of our study. First, this was a single centre study of select internal medicine clinics and conditions at an academic hospital under a universal healthcare system in Canada. Our findings may not be generalizable to other conditions or health care systems. Second, visit diagnoses were based on the most responsible diagnoses coded by the visit physician, and for some patients, multiple conditions could have been addressed in a single visit. It is possible that some patients followed for multiple conditions by the same provider (e.g. atrial fibrillation and heart failure) had varying visit diagnoses across different visits despite the same conditions repeatedly being addressed in the same visit. This may contribute to the trends in conditions seen in the pre-pandemic and pandemic periods. However, we expect this to affect a minority of patients and not substantially alter our findings. Third, we were unable to extract data on comorbidities and sociodemographic factors that may be associated with uptake of virtual care such as high disease burden, ethnicity, language, socioeconomic status, and under-housing. Other studies from the US have shown these factors to be associated with telemedicine use [ 21 , 31 , 36 , 37 ]; however we were limited to data available within the EMR. Finally, while this study examined trends in outpatient internal medicine visits and uptake of virtual care during the pandemic, we did not examine quality of care, outcomes, or patient satisfaction.

The COVID-19 pandemic led to a transition in the delivery of healthcare in Ontario from almost entirely in-person visits to over 80% virtual care in a short time period. Virtual care remained the predominant model of care delivery a year after the onset of the pandemic, and there were changes in the types of office visits and visit diagnoses seen during the pandemic. Several patient and visit factors were associated with greater uptake of virtual care. This study contributes to the growing literature on the use of virtual care and the effect of the pandemic on health services delivery. Future research is needed to understand drivers at the patient, provider, and system level led to decreased new consultations and increased follow-up visits. Data from other centres is needed to understand if our observation that patients with certain conditions were more likely to be seen virtually is replicated and understand the drivers behind these differences. Similarly, future research is needed on the equity of virtual care and how certain groups may be disadvantaged by the rapid shift towards virtual care. Finally, given the persistence of virtual care one year from pandemic onset as well as the multiple extensions of the temporary billing codes by the Ontario government, we suspect that virtual care will continue to be utilized in the future as part of the physician’s toolbox to provide quality care. However, this speaks to the importance of future work to understand the impact of virtual care on quality of care, patient outcomes, patient satisfaction, and cost-effectiveness of outpatient care.

Acknowledgements

The authors would like to thank Hayley Baranek, Patricia Rios, Xiao Zhou, Hye Rin Kim, Women’s Virtual Operations team, Department of Medicine at Women’s College Hospital for their support of this work. Dr. Payal Agarwal is supported by a New Investigator Award from the Department of Family Medicine, University of Toronto.

Abbreviations

Authors’ contributions.

CP, DN, CC, TO, ODL, RSB, GM contributed to the design of the study. CP and CC contributed to the analysis of data. CP, DN, CC, TO, ODL, PA, DM, RSB, GM contributed to the interpretation of the data, drafting and critical revision of the manuscript. All authors read and approved the final version of the manuscript.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. We would like to acknowledge Women’s College Hospital Institute for Health System Solutions & Virtual Care (WIHV) for in-kind resources provided.

Availability of data and materials

Declarations.

Not applicable.

None to declare.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Optimizing RVU Production In An Academic Medicine Practice

• Post author By James Allen, MD
• Post date June 8, 2019

The work RVU is the current medium of exchange in clinical practice for all physicians, both private and academic. And as the numbers of physicians employed by academic medical centers swells at the same time as the percentage of these physicians’ time dedicated to clinical practice grows, academic physicians in particular are under increasing pressure to maximize their RVU output. Consequently, many academic physicians find themselves struggling to produce their required numbers of RVUs. Historically, private practices were built around efficient RVU productivity but academic practices were not and consequently, the academic inpatient and outpatient practice environment and practice culture is not conducive to RVU maximization . Failure to meet annual RVU targets can result in loss of bonuses, salary reduction, career disillusionment, and general unhappiness. On the other hand, consistently meeting or exceeding RVU targets can provide job security and the freedom to chart one’s own career path in academic medicine. Here are some of the ways that academic physicians can optimize their RVU production.

In The Inpatient Setting:

• Don’t forget to submit your bill for your clinical services. This seems so simple but a few years ago, I did an analysis at our own hospital and found that 7% of inpatient services and procedures that were documented in the electronic medical record went unbilled. This was not because of a conspiracy by the physicians, it was simply because they forgot to enter a charge for a given day’s clinical work. It is easy to forget to submit a bill (often called the “charge capture” application in an electronic medical record). If you are busy trying to save a patient’s life, the lowest priority in your day is to put in a bill for that service. I consider myself pretty compulsive when it comes to billing and even I found times when I forgot to enter a bill for a consult, a return hospital visit, or a bedside procedure. Two strategies can help minimize forgotten charges: (1) work with your electronic medical record to create charge entry prompts when completing progress notes or procedure notes to make entering those charges easier and (2) develop a personal strategy to ensure that all services are billed each day – I print out a rounding list of all of my patients each day and note my E/M service & procedure charge on each patient as I enter charges; at the end of the day, I can take a quick look at the printout to confirm that every patient had a charge entered.
• Don’t avoid submitting a bill for your service. A number of years ago, one of our very best clinical educators stopped signing resident inpatient notes and inpatient charges. The excuse was that there just wasn’t enough time in the work day and it got in the way of bedside teaching. No note meant no bill for service. No bill meant no income. No income meant no job.
• Don’t  under bill.   Most large academic medical practices do billing audits by billing compliance personnel. These audits are largely defensive, designed to prevent over billing. This is because large medical practices (and particularly academic practices) are subject to billing audits by Medicare or other insurance companies. The bias from compliance audits is that it is better to err on the side of under billing than over billing. Over billing jeopardizes the organization but under billing jeopardizes the individual physician by making him/her do more work than is necessary to meet annual wRVU targets.

• There should be no such thing as a curbside consult. The curbside consult is when an admitting physician (or more likely a resident) asks an “off-the-record” clinical question of a consultant. There is no entry into the medical record by the consultant and there is no bill generated. If a consultant’s expert opinion is sought, that consultant should be paid for it. I was once an expert witness in defense of a university medical center. One of the residents had called a pathologist to ask an opinion about an inpatient case and made the mistake of documenting that conversation (and the pathologist’s name) in the medical record. The pathologist was named as a co-defendant in the malpractice suit. Even an off the record opinion can result in legal liability so you should bill for your expertise and opinion.
• Don’t sign-off too quickly. For many consulting physicians in academic practice, a major goal of the workday is getting the consult census list shortened as much as possible. Consult follow-up visits are beneficial to patient and the primary service because the consultant’s expertise can be applied to new test results and changes in the patient’s condition. This can reduce inpatient hospital length of stay. Those follow up inpatient encounters do not pay as much as initial consult encounters but they often take very little time and on a per-hour basis can generate more RVUs per hour than initial consults. Most initial inpatient consults require at least 2-3 follow-up visits and many will require daily follow-up visits until the patient is discharged. In academic practice, there is a strong tradition of being a “one and done” when it comes to consults. For a consultant, those follow-up visits take far less time than a follow-up visit by the admitting service (hospitalist, etc.) so you can perform a lot of follow-up visits in an hour.  I believe that this is the #1 low-hanging fruit in academic medicine for increased wRVU generation .

• Mundane tasks generate a lot of wRVUs but can melt your brain.   EKGs and pulmonary function tests are commonly performed in large medical centers. On an individual basis, neither generates very many work RVUs. However, they take very little time to interpret and document and consequently, the cardiologist or pulmonologist can generate huge numbers of wRVUs very quickly. The problem is that reading PFTs and EKGs is boring and are often seen as an unpleasant necessity of specialty practice. My brain would melt if the only thing I did all day was read PFTs but by reading them for an hour or two a week, I can generate enough wRVUs to free me up to do the uncompensated things that I really like to do.

• Make your EMR work for you.  Investing a little time developing disease-specific note templates, order sets, and order preference lists can pay enormous long-term benefits by creating time-saving shortcuts in your electronic medical record charting. I have different new consult templates for the inpatient conditions that I most commonly encounter: COPD exacerbations, pneumonia, asthma exacerbations, abnormal chest x-ray, pulmonary embolism, etc. I incorporate my own self-designed “smart lists” into the physical exam portion of my notes that default to the expected findings; for example, for an asthma consult note, the lung exam smart list defaults to “diffuse wheezing” whereas the pulmonary embolism consult lung exam smart list defaults to “normal breath sounds bilaterally”. This allows me to rapidly click through the physical exam and saves me precious keystrokes when creating my consult note. Copying and pasting can also shorten your documentation time but it can be hazardous if you are copying too much data from a previous day’s progress note because of the danger of importing out-of-date information (like vital signs, lab results, NPO status, etc.). By using templates for notes that automatically import new data into the daily note, you can avoid this. I limit my copying/pasting to just my “impression and plan” list so that I can remember what problems I am actively following and what my previous day’s recommendations were – I then edit the impression and plan as appropriate.

• You will get more efficient producing RVUs with age.   There is a Starling curve of physician productivity. It takes about 7 years after finishing residency or fellowship to get proficient in getting clinical work done. Not only do physicians continue to learn new knowledge but they get more efficient in getting their daily work done with everything from history taking to progress note writing. For most physicians, productivity peaks in their mid-50’s. After that, they often start dialing back the amount of time they spend in clinical practice.

In The Outpatient Setting:

• Pre-chart your patient encounters. Each outpatient encounter will require a certain amount of time in the patient exam room and a certain amount of time outside of the exam room. You can either finish your charting at the end of the day, after the patient leaves or you can do that additional charting before the patient arrives in the clinic. Either way, it will be the same amount of time – either before clinic or after clinic. But by pre-charting and preparing for the patient’s visit, you can often shorten the amount of time spent during actual clinic hours – this can free you up to spend more time communicating with individual patients and allow you to see more patients in a given period of time.
• Utilize CPT code 99358. This code is for “prolonged service without patient contact”. It requires documentation that you spent at least 31 minutes doing the service and I primarily use it (1) when reviewing a lot of medical records in advance of a new outpatient consult or (2) after an initial consult when I receive a lot of requested records and radiographic images. In my own practice, most new outpatients come with lots of chest x-ray & CT images that I need to review and interpret, office notes that I need to review, lab results I need to review, and pulmonary function tests that I need to review and interpret. About half of my new patients have > 31 minutes of records to be reviewed and documented. This CPT code is worth 2.10 wRVUs and when combined with a level 5 new outpatient visit (3.17 wRVUs), you can generate a whopping 5.27 wRVUs (7.91 total RVUs) for that visit. I use this code 2-3 times a week. Also, if that new patient does not show up, I still am able to generate some wRVUs for my efforts.
• Utilize the other CPT codes that you forgot to bill. The common ones are 99497 (advanced care planning, 30 minutes: 1.50 wRVUs), 99406 (smoking cessation 3-10 minutes: 0.24 wRVUs), 99495 (transition care management, moderate complexity: 2.11 wRVUs), and 99354 (prolonged services > 30 minutes: 2.33 wRVUs). I wrote about these and other often-overlooked CPT codes in a previous post .
• Cultivate a referral base. For specialists, new patients can come from self-referrals, emergency department referrals, or physician referrals. Self-referrals and ER referrals are notorious for being no-shows and for having no insurance (or having Medicaid). You are better off filling your schedule with referrals from primary care providers and other specialists because those patients are more likely to show up for their scheduled appointment and generally constitute a better payer mix. The best way to cultivate those referrals is by human contact, either introducing yourself in person or by the occasional phone call. Those referral physicians will remember your name the next time they need a consult if they have shaken your hand or heard your voice. This is especially true for nurse practitioner or physician assistant primary care practices – NPs and PAs don’t have the same opportunities to network with specialists at medical staff meetings, the hospital’s physician lounge, or CME events. A phone call to a primary care NP can endear you to him/her for life. Referral letters are also a good way to cultivate referrals. Each referral letter is an advertisement opportunity for your practice: a poorly constructed letter that consists of 4 pages of electronic medical record documentation will create animosity but a 1-paragraph readable note in prose form will create goodwill.
• Make the outpatient EMR work for you.   Reducing keystrokes saves you time that you can spend seeing more patients and generating more wRVUs. Just as in the inpatient setting, by creating note templates for common conditions that you use, you can reduce your documentation time; in my pulmonary practice, I have different templates for COPD, interstitial lung disease, asthma, abnormal x-ray, and bronchiectasis office notes. Pre-designed order preferences and smart lists can streamline your practice. Outpatient EMR optimization is a huge topic and I’ll devote a post just to this in the future.

• Convert patient phone calls into wRVUs. There are two ways to do this: get the patient into the office or use the new CPT code for telephone/EMR encounters. CPT code G2012 is for phone or EMR patient encounters that last 5-10 minutes for patients that are not seen for 7 days before or 24 hours after the phone/EMR encounter. It pays 0.25 wRVUs. The other strategy is to get those patients into the office – either at the end of the day or to fill in holes in the office schedule created by late cancelations. Alternatively, keep a open 15 or 30 minutes at the end of the day for add-on sick visits. I prescribe way too much steroids/antibiotics over the phone for COPD exacerbations, etc. that could at least be billed as a G2012.

• Use the entire day.   I often see physicians start their morning schedule at 9:00 even though the nurses and registration staff all arrive at 7:30. Similarly, I see physicians schedule their last patient at 3:30 or 4:00 even though the staff are paid to be there until 5:30. Time = wRVUs. Be sure to fill the entire day’s clinic time with patients.
• Double book strategically.   In my practice, there are almost always late cancelations and no-shows. By double booking a couple of slots in expectation of those cancelations and no-shows, you can ensure that the schedule stays full. I often see physicians double book at the beginning of their schedule – I think this is hazardous because if both patients show up, then the physician is behind the schedule for hours, creating exasperation for the physician and dissatisfaction for the patients. I think you are better off double booking a slot in the middle of the morning (or afternoon) and at the end of the day. this is because there are inevitably patients who show up 30 or 45 minutes early for their appointments so if there is a late cancelation, you can slip an early arriver into that slot, thus creating an opening in the middle of the afternoon (or morning) or at the end of the day that the double booked patient can fill.
• Make up canceled clinics. There should not be an expectation for making up clinics canceled for vacations and scheduled CME time off. However, in academic practice, there are always things that come up that conflict with the regular clinic times: academic retreats, medical staff meetings, visiting lecturers, new faculty candidate interviews, medical student lectures, etc. These activities fall under “academic time” (release time) and when those conflict with regular clinic time, necessitating canceling that afternoon’s clinic, then a make-up clinic should be scheduled. If your academic time temporarily displaces your usual clinic time then you should have an equal displacement of your usual academic time by make-up clinic time in order to keep your total weekly academic:clinic time ratio constant.
• Do point of care testing. For me, this means having an office spirometer (0.17 wRVUs per test). For others, it may mean an INR machine, an EKG machine, or a hemoglobin A1C machine.  In order to determine if you need a piece of equipment to do point of care outpatient testing, you have to do a pro forma that compares the cost of the equipment to the estimated income generated by that piece of equipment. It takes about 44 spirometry tests to pay for the cost of a spirometer, after that, all of the income generated by spirometry is profit.
• Partner with advanced practice providers. Everyone wants an NP/PA/LISW/pharmacist in order to make their practice more efficient and generate more wRVUs. But everyone also wants someone else to pay for that NP/PA/LISW/pharmacist. In a healthy clinical environment, the physician should work synergistically with advanced practice providers so that the total RVU productivity is greater than the sum of what that physician & advanced practice provider could generate operating individually. Examples are a physician assistant who does the post-op office visits so that the surgeon can do more surgeries or a nurse practitioner who sees routine follow-up heart failure visits so that the cardiologist can see more new patient consults that in turn lead to more cardiac stress tests and echos.

June 8, 2019

By James Allen, MD

I am a Professor Emeritus of Internal Medicine at the Ohio State University and former Medical Director of Ohio State University East Hospital

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Knowing how your services are valued is key to understanding and improving your productivity.

ANNE C. KANTNER, MD, FAAP, FACP

Fam Pract Manag. 2023;30(2):4-8

Author disclosure: no relevant financial relationships.

Although the use of value-based payment models is growing, 1 the most common payment method in physicians' employment contracts is still a base salary plus a production bonus based largely on relative value units (RVUs). 2 RVUs reflect a physician's volume of work and level of effort in treating patients. The more RVUs a physician generates, the more income the practice (and the physician) should receive.

Knowing the RVUs assigned to different types of services allows physicians to understand how their productivity is measured. And knowing their RVUs per visit and per year allows physicians to understand how they compare to their peers. They can then assess whether they need to see more patients, work more efficiently, or change key processes such as billing and coding to better reflect the amount of work they are doing.

The four questions outlined in this article can help physicians get started in understanding and improving their RVUs.

Knowing the work RVUs assigned to different types of services can help you understand how your productivity is being measured.

Work RVUs vary depending on the work and effort required; for example, a 99212 has 0.70 work RVUs while a 99214 has 1.92 work RVUs.

To improve your total work RVUs per year, you may need to work more efficiently so you can see more patients, improve your coding practices to capture the full value of your work, or offer more high-value services, such as procedures or transitional care management.

1. WHAT ARE RVUS?

RVUs are part of the Resource-Based Relative Value Scale (RBRVS), which Medicare and other payers have used for decades to determine payment for physician services. RVUs are just one of three components that factor into the payment calculation. 3 The other two components are geographic practice cost indices (GPCIs), which adjust RVUs to reflect regional differences in the cost of physician services, and a conversion factor (CF), which is the dollar amount by which total, geographically adjusted RVUs are multiplied to arrive at the payment amount for a given service. (See “ How payment for a service is calculated. ”)

Total RVUs are the sum of three types:

Work RVUs , which reflect the physician's work, including mental effort, decision making, technical skills, physical effort, stress related to patient risk, and amount of time spent,

Practice expense RVUs , which reflect the clinical and nonclinical labor costs and practice expenses,

Malpractice RVUs , which reflect the liability insurance costs.

The remainder of this article will focus on work RVUs because they are a key measure of physician productivity. Work RVUs for common services in primary care are listed at the end of this article. For example, a 99214 established patient evaluation and management (E/M) office visit has 1.92 work RVUs, a “Welcome to Medicare” visit has 2.6 work RVUs, and a 99223 initial hospital visit (admission) has 3.5 work RVUs.

HOW PAYMENT FOR A SERVICE IS CALCULATED

Relative value units (RVUs) are just one of three components that factor into physician payment under the Resource-Based Relative Value Scale:

Total RVUs , which is the sum of work RVUs, practice expense (PE) RVUs, and malpractice RVUs,

Geographic practice cost indices (GPCIs), which adjust RVUs to reflect regional differences in the cost of physician services,

A conversion factor (CF), which is the annually updated dollar amount by which total, geographically adjusted RVUs are multiplied to arrive at the payment amount for a given service.

Payments are calculated as follows:

[(Work RVUs x Work GPCI) + (PE RVUs x PE GPCI) + (Malpractice RVUs x Malpractice GPCI)] x CF = Payment amount

2. WHAT ARE YOUR WORK RVUS?

If you are an employed physician, you likely receive productivity reports from your employer, but you may not be in the habit of looking at them closely or you may not know what to do with the information. A helpful report should provide you with the following individualized data:

Annualized work RVUs (projected total if you continue to generate the same amount of work for the rest of the fiscal year),

Number of visits,

Annualized number of visits,

Types of visits you are seeing (such as preventive care visits, E/M visits, hospital visits, and Medicare wellness visits),

A breakdown of your E/M visits (i.e., what percentage are coded as level 1, 2, 3, 4, or 5).

If you are expected to meet a goal for work RVUs, you should receive your data on a regular and predictable basis — e.g., monthly, bimonthly, or quarterly — allowing you to analyze the data and make adjustments in scheduling, coding, or practice efficiency to meet the goal. Waiting until the end of the fiscal year to distribute a finalized report does not allow physicians enough time to make effective changes.

Data should be accurate, trustworthy, and presented to physicians in a user-friendly way. Practices should have a contact person who understands the information well and can answer physicians' questions or investigate discrepancies should they arise.

If your employer does not provide you with a productivity report, or does not provide it in a timely manner, ask for it. Most EHRs and practice management systems capture the required data and can generate customizable reports.

You can estimate your work RVUs on your own using the process described in the physician example .

HOW TO CALCULATE WORK RVUS PER VISIT AND PER YEAR: AN EXAMPLE

Dr. Smith is a family physician in a physician-owned outpatient practice. He sees an average of 10 patients per session, or 20 visits per day, and his average work RVUs are 1.69 per visit. Out of 260 total workdays per year, Dr. Smith has 40 days for vacation, continuing medical education, holidays, etc., leaving 220 workdays per year. That means he averages 4,400 visits and 7,436 work RVUs per year, which puts him well above the 50th percentile in national benchmarks but below the 90th percentile.

Here's the method used to estimate his work RVUs:

First, list the types of visits in an average session, assign work RVUs to each visit, and add the values to get the total work RVUs per session.

Next, complete the following calculations:

Work RVUs per session ÷ Visits per session = Work RVUs per visit

16.88 ÷ 10 = 1.69

Workdays per year x Visits per day = Visits per year

220 x 20 = 4,400

Visits per year x Work RVUs per visit = Work RVUs per year

4,400 x 1.69 = 7,436

3. HOW DO YOUR WORK RVUS COMPARE TO BENCHMARKS

Practices should establish work RVU benchmarks at the beginning of the fiscal year and include them in productivity reports so physicians can see whether they are meeting work RVU goals. Practices may use internal data to set benchmarks, comparing you to your peers, or they may rely on national benchmarks from external sources. Benchmarks should be specialty-specific, but they may differ for inpatient versus outpatient practice, new versus established physicians, and physician-owned practices versus practices owned by hospital or health systems.

For example, according to data from the Medical Group Management Association, for family medicine physicians in physician-owned outpatient practices, the 50th percentile is 5,945 annual work RVUs and the 90th percentile is 9,063. 4 Meanwhile, for those in hospital-owned or health-system-owned outpatient practice, the 50th percentile is 4,715 annual work RVUs and the 90th percentile is 7,451. One possible reason for the difference is that physician-owned practices may be more likely to reward productivity, while practices owned by a hospital or health system may be more likely to use straight salary compensation.

4. HOW CAN YOU INCREASE YOUR WORK RVUS?

If your work RVUs are below goal, there are several ways you may be able to increase them. Start by examining the following factors.

Visit volume . To assess whether low patient volume is contributing to low work RVUs, identify how many patients you see per day. A 2018 survey by the Physicians Foundation found an overall average of 20.2 patients per day across all specialties. 5 According to data from the American Academy of Family Physicians, family physicians average 84 patient encounters per week — 63 in the office, 12 e-visits, seven in the hospital, and two in nursing homes or house calls. 6 What ultimately matters is the benchmark your group is using. If your visit volume is too low, that does not necessarily mean that you need to work longer each day, but you may need to work more efficiently in order to see more patients. For example, you may need to delegate more tasks to your team, or you may need to address a high no-show rate.

Visit levels . Identify what percentage of your E/M visits are level 1 through 5, and compare this distribution to that of your peers. If your practice cannot provide this data, you can use national benchmarks derived from Centers for Medicare & Medicaid Services data (see this FPM worksheet , which has been updated with 2021 data). If you find that you are billing a higher percentage of lower-level visits than your peers, you will need to either see more patients to achieve the same overall work RVUs or investigate other issues. For example, it could be that your billing, coding, and documentation practices are inefficient, leading to under-coding or missed coding.

High-value visits . Identify which high-value visits you frequently provide, and which ones you should be providing. (See the list of work RVUs for different visit types on page 7 .) For example, new patient visits have higher work RVUs than established patient visits, so if you've closed your panel to new patients, this could be affecting your work RVUs. Many procedures are also valued highly, so you may want to consider expanding your scope of practice by adding skin procedures, joint aspirations and injections, treadmill stress tests, etc. Transitional care management visits and Medicare annual wellness visits are also higher-value services. Consider whether you can provide these to patients who need them, and how your care team can assist with the workload.

Visit lengths . Track your visit lengths for various visit types to see whether you need to adjust your scheduling practices. Adjusting visit duration in your scheduling template can allow you to see more patients per day or per session to meet your work RVU goals. For example, a physician who has a goal of 30 work RVUs per day and averages 1.3 work RVUs per visit (the equivalent of an established-patient, level 3 office visit) would need to see 23 patients per day. In an eight-hour workday, that would allow for an average of 20-minute visits. Note that time-based billing became easier following the 2021 E/M coding changes and now includes the physician's total time spent on the day of the visit (not only face-to-face time but also pre-visit planning, coordination of care after the visit, and other physician activities).

WORK RVUS FOR COMMON VISIT TYPES

FOCUSING ON WHAT YOU CAN CONTROL

Some aspects of physician productivity are beyond your control. For example, you can't really control whether your schedule is full every day or what level of care your patients need. However, it's still beneficial to understand the factors that are affecting your work RVUs so you can assess your performance and make the case for what you need, such as more clinical support staff or a second exam room. Improving productivity isn't just about working harder, but working smarter and identifying changes in practice operations and efficiency that will actually make a difference.

Johnson K, Rittenhouse D. From volume to value: progress, rationale, and guiding principles. Fam Pract Manag . 2023;30(1):5-7.

Singleton T, Miller P. Employment and contract considerations for family physicians in the era of COVID-19. Fam Pract Manag . 2021;28(1):11-16.

What are relative value units? AAPC. Updated June 21, 2022. Accessed Jan. 27, 2023. https://www.aapc.com/practice-management/rvus.aspx

Medical Group Management Association. DataDive provider compensation: 2022 report based on 2021 data; 2022. https://www.mgma.com/data/data-reports/2022-mgma-data-dive-provider-comp-report

• 2018 survey of America's physicians. The Physicians Foundation and Merritt Hawkins. September 2018. Accessed Jan. 27, 2023. https://physiciansfoundation.org/physician-and-patient-surveys/the-physicians-foundation-2018-physician-survey

Slideshow: a week in the life of a family physician. FPM . 2020. Accessed Jan. 27, 2023. https://www.aafp.org/pubs/fpm/multimedia/slideshows/fp-hours-compensation.html

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