Search Journal-type in search term and press enter
Announcements and Recruitment
Social Media-Follow Southwest Journal of Pulmonary and Critical Care on Facebook and Twitter

General Medicine

(Click on title to be directed to posting, most recent listed first)

Finding a Mentor: The Complete Examination of an Online Academic 
   Matchmaking Tool for Physician-Faculty
Make Your Own Mistakes
Professionalism: Capacity, Empathy, Humility and Overall Attitude
Professionalism: Secondary Goals 
Professionalism: Definition and Qualities
Professionalism: Introduction
The Unfulfilled Promise of the Quality Movement
A Comparison Between Hospital Rankings and Outcomes Data
Profiles in Medical Courage: John Snow and the Courage of
   Conviction
Comparisons between Medicare Mortality, Readmission and 
  Complications
In Vitro Versus In Vivo Culture Sensitivities:
   An Unchecked Assumption?
Profiles in Medical Courage: Thomas Kummet and the Courage to
   Fight Bureaucracy
Profiles in Medical Courage: The Courage to Serve
   and Jamie Garcia
Profiles in Medical Courage: Women’s Rights and Sima Samar
Profiles in Medical Courage: Causation and Austin Bradford Hill
Profiles in Medical Courage: Evidence-Based 
   Medicine and Archie Cochrane
Profiles of Medical Courage: The Courage to Experiment and 
   Barry Marshall
Profiles in Medical Courage: Joseph Goldberger,
   the Sharecropper’s Plague, Science and Prejudice
Profiles in Medical Courage: Peter Wilmshurst,
   the Physician Fugitive
Correlation between Patient Outcomes and Clinical Costs
   in the VA Healthcare System
Profiles in Medical Courage: Of Mice, Maggots 
   and Steve Klotz
Profiles in Medical Courage: Michael Wilkins
   and the Willowbrook School
Relationship Between The Veterans Healthcare Administration
   Hospital Performance Measures And Outcomes 

 

Although the Southwest Journal of Pulmonary and Critical Care was started as a pulmonary/critical care/sleep journal, we have received and continue to receive submissions that are of general medical interest. For this reason, a new section entitled General Medicine was created on 3/14/12. Some articles were moved from pulmonary to this new section since it was felt they fit better into this category.

-------------------------------------------------------------------------------------

Entries in physician (3)

Monday
Dec082014

Finding a Mentor: The Complete Examination of an Online Academic Matchmaking Tool for Physician-Faculty

Guadalupe F. Martinez, PhD1

Jeffery Lisse, MD1

Karen Spear-Ellinwood, PhD, JD2

Mindy Fain, MD1

Tejo Vemulapalli, MD1

Harold Szerlip, MD3

Kenneth S. Knox, MD1

 

1Departments of Medicine and 2Obstetrics and Gynecology, University of Arizona, Tucson, AZ

3Department of Medicine, University of North Texas Health Science Center Department of Medicine, Fort Worth, TX

 

Abstract

Background: To have a successful career in academic medicine, finding a mentor is critical for physician-faculty. However, finding the most appropriate mentor can be challenging for junior faculty. As identifying a mentor pool and improving the search process are paramount to both a mentoring program’s success, and the academic medical community, innovative methods that optimize mentees’ searches are needed. This cross-sectional study examines the search and match process for just over 60 junior physician-faculty mentees participating in a department-based junior faculty mentoring program. To extend beyond traditional approaches to connect new faculty with mentors, we implement and examine an online matchmaking technology that aids their search and match process.

Methods: We describe the software used and events leading to implementation. A concurrent mixed method design was applied wherein quantitative and qualitative data, collected via e-surveys, provide a comprehensive analysis of primary usage patterns, decision making, and participants’ satisfaction with the approach.

Results: Mentees reported using the software to primarily search for potential mentors in and out of their department, followed by negotiating their primary mentor selection with their division chief’s recommendations with those of the software, and finally, using online recommendations for self-matching as appropriate. Mentees found the online service to be user-friendly while allowing for a non-threatening introduction to busy senior mentors.

Conclusions: Our approach is a step toward examining the use of technology in the search and match process for junior physician-faculty. Findings underscore the complexity of the search and match process.

Introduction

Across the spectrum of disciplines within the academy, it is well documented that mentorship is key to career advancement and satisfaction among faculty (1). For physician-faculty, mentoring is “considered to be a core component of the faculty duties…to fulfill…th(e) academic medicine mission” (2). Although important, structural barriers to mentorship still exist (2,3). Finding an appropriate mentor is critical not only in establishing a productive and engaging mentorship, but in having a successful career in academic medicine (4). However, scholars note that finding the most appropriate person is not without its challenges: especially for junior faculty (3-7,9). Some studies find that junior faculty (and faculty new to institutions) depict the search process as the most difficult step in establishing a mentorship (3,7,9,10). In these studies, mentees recommend a match process that begins with a comprehensive list of potential mentors that includes contact information (3,7). Although noteworthy, this recommendation fails to elaborate on the extent to which a mere list could improve the search and match process. How such lists are implemented or if supplemental mechanisms were used to connect unfamiliar faculty is unclear.

Prior literature stresses the importance of “effort and persistence” when embarking on a search (3,4,9). Through this seemingly daunting process, scholars specifically advise mentees to ask colleagues to connect them to others with similar interests, and invest time into researching the backgrounds of potential mentors to determine their suitability. However, there are inherent challenges to this approach. First, the time spent investigating mentor backgrounds may vary greatly depending on the number and quality of resources available to conduct such an investigation. Second, mentees new to an institution could find it difficult and/or unproductive to ask new colleagues to connect them to potential mentors as colleagues may not be able to make an appropriate connection if they are unfamiliar with the mentor pool. Although this could point mentees in the right direction, they could spend an inordinate amount of time meeting with numerous contacts only to find academic and clinical interests to be unrelated or tangentially related to theirs. Previous studies found that mentees who self-match with a mentor, are more likely to be satisfied with their mentorship experience (3,4,7,8). Yet, if the institutional mentoring culture functions as described above, mentees would have to rely solely on their division chief or department chair for an assigned mentor. This could be problematic if the chief or chair is unfamiliar with the strengths of the mentor pool.

In hallmark studies by Williams et al. (7), and Straus et al. (3), they highlight perceived barriers to mentorship from the mentee perspective, and find those to be: a) a lack of local and adequate mentor selection, b) time constraints for the mentors, c) inadequate access, and d) a lack of formal programs and mechanisms to connect faculty. Straus et al.’s (3) study also sheds light on mentees desire to choose a mentor instead of being assigned. They find that mentees perceive assigned partnerships as superficial, but that assigned matches are sometimes useful because the search process is challenging for those new to an institution. Given the conflicting perceptions, these authors call for additional strategies to improve the search and match process as well as an examination of those strategies. Methods to optimize mentees’ time and diversify searches have yet to be delineated. More importantly, the role technology could play in mentoring remains understudied. As identifying the mentor pool and improving the search process are paramount to both, a mentoring program’s success, and the academic medical community, innovative approaches are needed.

We build on the work of Straus et al. (3), and Sambunjak et al. (10) by examining the search and match process for physician-faculty mentees participating in our department-based mentoring program. In our cross-sectional study we seek to better understand internal matching behaviors and the role technology could play. We detail and explore technology aimed at improving the search and match process for our mentees. This “matching” tool further advances our knowledge about the role technology could (or could not) play in addressing the challenges associated with the search and match process. Our research questions ask: If a “matching” tool is implemented, what would the matching behavior be within the department? What are the primary usage patterns among mentees? How receptive have mentees been in adopting this mechanism to aid their search and matching efforts?

Methods

The University of Arizona’s Department of Medicine developed a department-based faculty mentoring program in March 2011 during which a needs assessment was conducted on junior physician-faculty. First, like Straus et al.’s (3) findings, mentees partaking in our needs assessment desired assistance with the search and match process. Mentees reported a lack of knowledge about available mentors, their areas of expertise, and difficulty establishing contact with senior faculty.  The committee concluded experimenting with a computer program that functioned much like an online matchmaking service would improve the process; extending matching beyond the common strategies of contact list distribution, top down assignments, and informal social forums. The committee then customized an online matchmaking program, Mentor Match© (Intrafinity Inc., Ontario), to create a “virtual space” for mentor and mentee use. The committee crafted a “one-stop shop” where faculty accessed mentor/ mentee profiles containing academic interests, department mentoring events, and mentorship contract templates (Figure 1).

Figure 1. University of Arizona department of medicine opening user console view.

It was suspected that our faculty demographics included an overrepresentation of junior faculty (assistant professor rank) as compared with the number of senior faculty (associate and full professor rank) (11,12).  Also evident was that commitments to medical students and trainees prevented senior faculty from being able to devote sufficient time to mentor junior faculty. As such, the committee piloted an interdisciplinary approach and included mentors outside the department and College of Medicine to compensate for the low number of available mentors in Medicine (e.g. Public Health).

Methodology

A concurrent mixed method design was applied. We triangulated quantitative (numerical) and qualitative (descriptive) data to provide a comprehensive analysis of the primary usage patterns related to search and match behavior, and understand satisfaction with the online tool (13). We generalized results to our sample and then explored nuances based on narrative feedback.

Implementation

With the official launch of the mentoring program in January 2012, Mentor Match© went live to connect over 100 physician-faculty and faculty-researchers. At this time, the Department of Medicine had 65 junior faculty in search of mentors. A combined total of 54 mentors (N=32 full professors; N=22 associate professors) from the Department of Medicine, Department of Emergency Medicine, and College of Public Health served as mentors for this group.

Faculty profiles include email addresses and detailed background information about each faculty member (e.g. academic track, age range, overall years teaching) (Figures 2 and 3).

Figure 2. University of Arizona department of medicine mentor/mentee profile and skills inventory.

Figure 3. University of Arizona department of medicine mentor/mentee profile and skills inventory.

Once faculty data is entered, Mentor Match© produces a complete listing of top recommended mentors based on similarities between mentees and mentors. One-on-one demonstration of how Mentor Match© works occurs during new faculty orientation. Current CVs are uploaded and available for in depth review of publication record, training history and current funding. Junior faculty can also access other junior faculty profiles in the department to form peer mentoring groups.

Participants, data, and analysis

Voluntary mid-year and annual assessments are components of the mentoring program. IRB approved questionnaires developed by the committee were disseminated to program participants as part of a broader study and program quality control. For ongoing program evaluation and to inform the committee, we collected data from five sources: a) committee meeting minutes, b) observation notes, c) human resources faculty rosters from 2011-2012; 2012-2013, d) 2011 junior faculty needs assessment report, and e) voluntary end-of-the-year questionnaires.

Study participants included only mentee MD’s, DO’s, PhD’s, MD/PhD’s, and MD/MPH’s with the rank of Assistant Professor, Lecturer or Research Scholar in the Department of Medicine on one of three faculty tracks: clinical-educator, clinical, and research.  

Cross tabulations formulated in SPSSv21 were used as part of survey analysis to compare categorical data from faculty rosters and questionnaires relevant to matching behavior and usage patterns. Qualitatively, document analysis using thematic coding for trend identification was conducted using Nvivo 10 to analyze narrative comments. Similarly, document analysis and thematic coding was implemented on committee meeting minutes, observation notes, and faculty roster to report the events and decision making process involved in the implementation of the program and matching tool (Figures 4 and 5).

Figure 4. Mentor match questionnaire (end-of-year).

 

Figure 5. Analysis coding scheme for setting description in methods and results.

 

Results

The program began with 65 mentees in January 2012. After annual faculty attrition, 72% of mentees (44/61) reported using the software and completed the voluntary end-of-year questionnaire in January 2013.

Selection patterns

Mentees were asked to report their primary use of Mentor Match©. Three usage patterns were apparent (Appendix D, Table 1.0a). Over half of mentees reported primarily using the software to search for potential mentors both in and out of the department. Almost a third of mentees reported mainly using the software to search for potential mentors within the department only. Just under 10% (4/44) of mentees reported primary usage of the software to expand their professional peer network. Slightly over half of males utilized the software to search for potential mentors both in and out of the department. However, among females, this latter usage pattern was even more prevalent (17/25; 68%). Among those reporting primary usage to search for professional network expansion, males reported this practice at a disproportionately higher rate (3/19; 15%) than that of their female counterparts (1/25; 4%).

While mentees considered the recommended list of potential mentors from Mentor Match© in their match decision, just over half reported negotiating their primary mentor selection with their division chief’s recommendations (25/44; 57%). This means that mentees discussed their search results and interests with their chief to come to an agreement about who would serve as their mentor (Tables 1-3).

Table 1. Questionnaire results: gender.

 

Table 2. Questionnaire results: Search and matching behavior after Mentor Match© implementation in the department primary use and gender cross tabulation.

 

Table 3. Match results and gender cross tabulation.

 

In this “negotiation” the mentee and chief come to a consensus instead of the chief assigning a partnership with no input from the mentee, a relatively common practice prior to this mentoring initiative. For the mentee, there is a sense of self-matching with guidance from the chief. This match pattern occurred proportionate to the respective totals of male and female mentees. An extremely small minority of junior faculty, all males, did not have mentors at the time of data collection (2/19; 10.5%). Finally, the next most common match patterns were the forced assignment (9/44; 20%) followed by the self-matched (8/44; 18%). At almost an even rate, female (5/25; 20%) and male (4/19; 21%) mentees reported considering the software’s top recommended mentors, but were ultimately assigned a mentor by their division chief. Remaining mentees (4/25; 16% females and 4/19; 21% males) reported considering the software’s recommendations, but eventually self-matched to a mentor of their choice.

Mentee feedback

The vast majority of mentees (40/44; 91%) found the software user-friendly, reporting that they would use the software for ongoing searches (Table 4). Questionnaire comments included positive feedback. Mentees’ appreciated the: a) non-threatening forum enabling access to detailed information about potential mentors, b) forum’s convenience, and c) functionality allowing access to research scholars outside the department. Finally, recommended improvements called for introductory training on website navigation, and viewing access to junior peer profiles.

Table 4. Mentee feedback.

 

Discussion

Building on Zerzan et al.’s (9) guide, we provide a robust description of implementing a software-based mentoring program. This software serves as a faculty directory and matching tool to facilitate mentors-mentee relationships in a large clinical department. Our systematic approach toward matching is a first step toward examining the use of technology to ease the search and match process for junior physician-faculty. We discovered that a “negotiated approach”, where junior faculty Mentor Match© selections were then explicitly discussed with division chiefs and department heads, was highly used and valued. Our data suggest that knowledge of local organizational culture or other information that can only be imparted through discussions with their chiefs and colleagues, are also highly valued.

Sambunjak et al.’s (10) qualitative study highlights the complexity of navigating partnerships. Our findings extend these observations to the search and match process, which is just as complex. More in-depth examination of the decision making process for those using software based matching or self-matching is needed to better understand what leads to junior faculty securing successful mentoring relationships. The shortage of mentors found in our needs assessment mirrored findings from national studies,11,12 implying that mentoring junior faculty is a challenge or not a priority compared to students, residents, and fellows. Given today’s heavy emphasis on clinical productivity and formal responsibilities teaching \ trainees at all levels, inspiring senior faculty to mentor junior faculty could be particularly difficult (5,15).  Departmental leaders and program administrators must realize mentor shortages will impact the search experience regardless of methodology employed. The consequences of not addressing barriers in mentorship may include frustration with the search process, junior faculty turnover, and erosion of an important part of the academic culture. In addition to heeding recommendations by Straus et al. (3) of providing protected time and formal recognition for mentoring, departments should foster interdisciplinary networks inside and outside of the medical discipline, leverage the emeritus professor workforce, and embrace mentor panels. Technology based mentor searches could facilitate implementation of such initiatives with the goal of improving professional satisfaction among mentees.

Limitations

Our study examines the usage patterns of and feedback on Mentor Match© from the junior faculty mentee perspective, but there are limitations. First, we have not assessed whether and how mentors use Mentor Match© to research mentees who have reached out to them. Knowing if immediate access to mentees’ backgrounds and skills assists mentors in deciding whether to accept a mentorship or refer them to a colleague could inform us about the potential benefits of this software tool for mentors. This study also draws on a small mentee self-reporting sample in one department with just over half of all junior faculty participating. Although the sample is small, particularly regarding software feedback, findings provide a starting point to learn the technological needs of faculty related to the search and match challenge. Such data helps us tailor online profiles and site navigation. Finally, we also do not know whether there is a significant advantage to “negotiated” mentorships as compared with those established solely by using Mentor Match©.

Despite these limitations our study is the first to assess the role technology could play in the search and match process for physician-faculty. Casting the online matchmaking net more broadly to include other colleges and including trainees could add another dimension toward understanding how to improve the search and matching process in academic medicine.  

Conclusion

Our study details Mentor Match© implementation and illustrates that software driven approaches can assist physician-faculty in establishing mentoring relationships. This approach may complement other search and matching efforts ongoing in departments and may be used to connect faculty across disciplines. In general, this tool continues to have a positive impact in our department, helping to achieve our goal of facilitating and expanding the mentee’s professional networks.

Acknowledgments

Role of each author in manuscript preparation:

  • Dr. Martinez is the lead author of this paper. Participation included mentoring program committee membership, IRB documentation, data collection, study design, analysis, initial manuscript draft, revision implementation, approve final version.
  • Dr. Lisse’s participation included mentoring program committee membership, questionnaire design, manuscript review/editing, approve final version.
  • Dr. Spear-Ellinwood’s participation included data member checking, manuscript review/editing, approve final version.
  • Dr. Fain’s participation included mentoring program committee membership, questionnaire design, study design, manuscript review, approve final version.
  • Dr. Vemulapalli’s participation included mentoring program committee membership, questionnaire design, study design, approve final version.
  • Dr. Szerlip’s participation included chairing the mentoring program committee, manuscript review/editing, approve final version.
  • Dr. Knox is the senior mentor on this paper. Participation included mentoring program committee membership, IRB documentation review, study design, questionnaire design, manuscript review/editing, approve final version.

Funding:

This study was partially funded by an internal educational research award by the University of Arizona College of Medicine Academy of Medical Education Scholars in November 2012 and the Department of Medicine Administration.

Secondary Publication Notice:

This descriptive article is an unabridged report. A 500 word version of the full length manuscript is under review for primary publication in Medical Education’s Really Good Stuff section. This section presents short reports that illustrate general lessons learned from innovation in medical education, and include very little data and description.

References

  1. Savage HE, Karp RS, Logue R. Faculty mentorship at colleges and universities. College Teaching. 2004;52(1):21-4. [CrossRef]
  2. Sambunjak D, Straus SE, Marusie, A. Mentoring in academic medicine: A systematic review. JAMA. 2006;6(9):1103-15. [CrossRef] [PubMed]
  3. Straus SE, Chatur F, Taylor M. Issues in the mentor-mentee relationship in academic medicine: A qualitative study. Acad Med. 2009;84(1):135-9. [CrossRef] [PubMed]
  4. Jackson VA, Palepu A, Szalacha L, Caswell C, Carr PL, Inui T. Having the right chemistry: A qualitative study of mentoring in academic medicine. Acad Med. 2003;78(3):328-34. [CrossRef] [PubMed]
  5. Pololi L, Knight S. Mentoring faculty in academic medicine: A new paradigm? J Gen Intern Med. 2005;20(9):866-70. [CrossRef] [PubMed]
  6. Benson CA, Morahan PS, Sachdeva AK, Richman RC. Effective faculty preceptoring and mentoring during reorganization of an academic medical center. Med Teach. 2002; 24:550-7. [CrossRef] [PubMed]
  7. Williams LL, Levine JB, Malhotra S, Holtzheimer P. The good-enough mentoring relationship. Acad Psychiatry. 2004;28:111–5. [CrossRef] [PubMed]
  8. Yamada K, Slantez PJ, Boiselle PM. Perceived benefits of a radiology resident mentoring program: Comparison of residents with self-selected vs assigned mentors. Can Assoc Radiol J. 2014;65(2):186-91. [CrossRef] [PubMed]
  9. Zerzan JT, Hess R, Schur E, Phillips, RS, and Rigotti, N. Making the most of mentors: A guide for mentees. Acad Med. 2009;84(1):140-4. [CrossRef] [PubMed]
  10. Sambunjak D, Straus SE, Marusic A. A systematic review of qualitative research on the meaning and characteristics of mentoring in academic medicine. J Gen Intern Med. 2010;25(1):72-78. [CrossRef] [PubMed]
  11. Data and Analysis: Faculty roster. Distribution of full-time faculty by department, rank, and gender. 2012. Available at:  https://www.aamc.org/download/305522/data/2012_table3.pdf (accessed 12/8/14).
  12. Stacy J, Williams LP, Blair-Loy M. Medical professions: The status of women and men Center for Research on Gender in the Professions University of California- San Diego; 2013. Available at: http://crgp.ucsd.edu (accessed 12/8/14).
  13. Creswell JW. Research design: qualitative, quantitative, and mixed methods approaches 3rd edition. Thousand Oaks: Sage Publications, Inc.; 2009.
  14. DeCastro R, Sambuco D, Ubel PA, Stewart A, Jagsi R. Mentor networks in academic medicine: moving beyond a dyadic conception of mentoring for junior faculty researchers. Acad Med. 2013;88(4):488-96. [CrossRef] [PubMed]
  15. Berger TJ, Ander DS, Terrell ML, Berle DC. The impact of the demand for clinical productivity on student teaching in academic emergency departments. Acad Emerg Med. 2004;11:1364-7. [CrossRef] [PubMed]

Reference as: Martinez GF, Lisse J, Spear-Ellinwood K, Fain M, Vemulapalli T, Szerlip H, Knox KS. Finding a mentor: the complete examination of an online academic matchmaking tool for physician-faculty. Southwest J Pulm Crit Care. 2014;9(6):320-32. doi: http://dx.doi.org/10.13175/swjpcc138-14 PDF

Saturday
May242014

Professionalism: Introduction

Robert A. Raschke, MD

Banner Good Samaritan Medical Center

Phoenix, AZ

Editor's note: This is the first of a multi-part series on professionalism. The remaining parts will be posted over the next few weeks.

An important event in my career occurred about 20 years ago, late on a Friday afternoon. I was scheduled on call in the ICU for the entire 72-hour weekend, and even though I was just getting started, I was already tired and in a lousy mood. At 5 PM, I got a consult to see a patient in the neuro ICU. He was a 34-year-old man who had attempted suicide by drinking ethylene glycol antifreeze after an argument with his girlfriend. He had initially stabilized from a medical standpoint, but then developed delayed-onset cerebral edema. The team that was taking care of him had unsuccessfully pursued all treatment options. After 8 days of effort, he remained in a deep coma, near brain death. Now, with nothing left to try, and no hope left for a good outcome, they were dumping responsibility onto me just in time for the weekend.

I considered this unhappily as I began to page through his thick chart, trying to suppress my frustration so that I could concentrate, but I was interrupted by the patient's nurse – Terry - before I could get very far. She told me that the patient's mom had just stormed into the unit, and was demanding to talk with her son’s doctor - which as of the last 10 minutes was now me.  She warned me that the patient’s mother was inpatient, accusatory and totally unrealistic about her son's prognosis, but despite all this, Terry acted somewhat relieved that I was there. The impression that she was somehow happy about the situation made me even more angry than I already was. 

I had had enough. I really gave Terry an earful– outlining all my suspicions about the bad motivations of the referring team and concluding with my refusal to do their dirty work. Somehow, in my self-centeredness, I expected her to empathize with me. But she didn't. Instead, she appeared to be somewhat shocked and deflated. She listened silently to my rant, then turned and walked away without saying anything.

It took me a few minutes to realize that she had a higher opinion of me than I had of myself. She had thought I was a good doctor– strong enough to shoulder a tough situation– compassionate and empathic for a bereaved mother - ready to take on this challenge and make a bad situation a little better. I had proved her wrong.

I always thought of myself as a good doctor, but I realized then that I really wasn't all that good. I composed myself and tried to reset my thinking. I introduced myself to the patient’s mother briefly after explaining that I hadn't had time to review all the records– later, we would sit down and really talk. She actually wasn’t as unreasonable as I imagined she might be. It turned out I did have an important job to do in this case– to help a grieving mother come to terms with the death of her beloved son. The next day I apologized to Terry– this turned out to be a good long-term investment, since we continue to work together to this day.

This was an experience that got me thinking about how I could try to become a better doctor. Not by studying in order to get smarter, but by having the proper goals and attitude– the things this series is about.  Recounting this story also gives me the opportunity to admit that I claim no special personal legitimacy to write a series for SWJPCC on professionalism. I am pretty lazy at times. I have a temper when I’m under pressure. I can sometimes be hurtful to nurses and residents. There are even a few people who would consider it the height of hypocrisy for me to come off like I know anything about being good.  During the week in which I first began writing this section, I did a bunch of very unprofessional things– things I was ashamed of them even as I was proceeding forward with them:

  1. I got a page about a patient that was deteriorating just as I sat down to a very nice lunch. The patient was a young, otherwise– healthy alcoholic. I decided to relax and finish my lunch before heading up to see him. By time I finished dessert, he had deteriorated and was extremely unstable. 
  2. I had misgivings about a patient’s DNR status. I thought the family might rescind the DNR order if they fully understood the clinical situation. But I didn’t want them to rescind DNR status, so I purposely avoided talking to them. 
  3. I missed the essential (and not obscure) physical finding of abdominal pain in a patient with septic shock on steroids– a clinical mistake that I’ve repeatedly lectured others about during Mortality and Morbidity conference. This error delayed diagnosis of a life-threatening bowel perforation.
  4. I declined a personal invitation to attend the memorial service of a patient that I felt very close to– who had in fact asked me for a hug the last time I had seen her before she died. Instead, I sat at home and watched TV.

So no, I am not an expert at professionalism. But I do care about it. So I am not going to write about the doctor I am, but about the doctor I want to be. Please look at this series in that spirit and do not allow my personal shortcomings to undermine our consideration of this topic.

Why discuss professionalism in medicine? I've considered the possibility that the age of professionalism is over– that talking about it is like trying to get your kids interested in playing the board-game Monopoly. Technology is the thing nowadays. It’s incredibly satisfying to help save a patient’s life with ECMO in the ICU. Yet some technological advances increasingly distance us from our patients.     

I have heard that when Laennec invented the stethoscope in 1816, there was widespread concern about the negative effect it might have on the doctor-patient relationship. Prior to the invention of the stethoscope, doctors placed their ear directly upon the patient's chest to listen to the heart and lungs. At this point in history, the stethoscope actually came between the doctor and patient– a barrier to the intimacy of the physical examination.

In a modern ICU, all patients are under "standard precautions" for infectious disease control– this means doctors and nurses are supposed to wear gloves when we shake their hand. Other infection control precautions require that masks, eye-shields and gowns be worn inside patient rooms. When we employ a proning bed, the patient is totally cocooned– it’s is difficult to even see a patient inside a prone bed, much less touch them.

Telemedicine is increasingly incorporated into patient care– this allows a physician anywhere in the world to take care of patients in our hospital remotely, utilizing video cameras. Mobile devices– almost like robots– with a face display video screen for a head, can be wheeled into a patient’s room to facilitate electronic interactions between doctors and patients.

The advent of the hospitalist has all but destroyed the traditional continuity of the doctor patient relationship. Patients who are sick enough to land in the hospital are rarely seen by their family doctor. Within the hospital, many doctors (including myself) work shifts– taking care of individual patients only within the time slots of their work schedule. Technically, my responsibility for my patients ends at "quitting time”.

More physicians are employed by healthcare systems than ever before. The choices that patients and doctors once made together are thereby increasingly influenced by non-physician administrators. Politicians have increasingly attempted to create financial incentives for doctors to behave as they think we should behave. The very semantics of related constructs such as the “physician report card” diminishes us as a profession, turning us back to a time before we could be trusted to know and do what was best for our patients.

I think it's fair to say that the risk that might lose our professionalism, our humanism, has never been greater than it is at this point in the history of medicine. So there has probably never been a better time to reconsider professionalism as an essential part of being a doctor.

Many of us were taught in medical school about how to “act professional” – maintaining a detached demeanor, not allowing yourself to get emotionally-involved, appearing confident in all situations, etc. That’s not the kind of professionalism I’m going to talk about. Sir William Osler once said “the secret to the care of the patient is in caring for the patient” I think that’s a much better place to start our consideration of professionalism.

In the next installment we will consider the Oath of Maimonides and how it applies to the practice of medicine in a modern ICU:

"The eternal providence has appointed me to watch over the life and health of Thy creatures.

May the love for my art actuate me at all time; may neither avarice nor miserliness, nor thirst for glory or for a great reputation engage my mind; for the enemies of truth and philanthropy could easily deceive me and make me forgetful of my lofty aim of doing good to Thy children.

May I never see in the patient anything but a fellow creature in pain.

Grant me the strength, time and opportunity always to correct what I have acquired, always to extend its domain; for knowledge is immense and the spirit of man can extend indefinitely to enrich itself daily with new requirements. Today he can discover his errors of yesterday and tomorrow he can obtain a new light on what he thinks himself sure of today.

Oh, God, Thou has appointed me to watch over the life and death of Thy creatures; here am I ready for my vocation and now I turn unto my calling."

Reference as: Raschke RA. Professionaism: introduction. Southwest J Pulm Crit Care. 2014;8(5):284-7. doi: http://dx.doi.org/10.13175/swjpcc067-14 PDF

Friday
Apr062012

Correlation between Patient Outcomes and Clinical Costs in the VA Healthcare System

Richard A. Robbins, M.D.1

Richard Gerkin, M.D.2

Clement U. Singarajah, M.D.1

1Phoenix Pulmonary and Critical Care Medicine Research and Education Foundation and 2Banner Good Samaritan Medical Center, Phoenix, AZ

 

Abstract

Introduction: Increased nursing staffing levels have previously been associated with improved patient outcomes.  However, the effects of physician staffing and other clinical care costs on clinical outcomes are unknown.

Methods: Databases from the Department of Veterans Affairs were searched for clinical outcome data including 30-day standardized mortality rate (SMR), observed minus expected length of stay (OMELOS) and readmission rate. These were correlated with costs including total, drug, lab, radiology, physician (MD), and registered nurse (RN), other clinical personnel costs and non-direct care costs.

Results: Relevant data were obtained from 105 medical centers. Higher total costs correlated with lower intensive care unit (ICU) SMR (r=-0.2779, p<0.05) but not acute care (hospital) SMR. Higher costs for lab, radiology, MD and other direct care staff costs and total direct care costs correlated with lower ICU and acute care SMR (p<0.05, all comparisons). Higher RN costs correlated only with ICU SMR. None of the clinical care costs correlated with ICU or acute care OMELOS with the exception of higher MD costs correlating with longer OMELOS. Higher clinical costs correlated with higher readmission rates (p<0.05, all comparisons). Nonclinical care costs (total costs minus direct clinical care costs) did not correlate with any outcome.

Conclusions: Monies spent on clinical care generally improve SMR. Monies spent on nonclinical care generally do not correlate with outcomes.

Introduction

Previous studies have demonstrated that decreased nurse staffing adversely affects patient outcomes including mortality in some studies (1-5). However, these studies have been criticized because studies are typically cross-sectional in design and do not account for differences in patients’ requirements for nursing care. Other observers have asked whether differences in mortality are linked not to nursing but to unmeasured variables correlated with nurse staffing (6-9). In this context, we correlate mortality with costs associated with other clinical expenditures including drug, lab, radiology, physician (MD), and other clinical personnel costs.

The observed minus the expected length of stay (OMELOS) and readmission rates are two outcome measures that are thought to measure quality of care. It is often assumed that increased OMELOS or readmission rates are associated with increased expenditures (10,11). However, data demonstrating this association are scant. Therefore, we also examined clinical care costs with OMELOS and readmission rates.

Methods

The study was approved by the Western IRB.  

Hospital level of care. For descriptive purposes, hospitals were grouped into levels of care. These are classified into 4 levels: highly complex (level 1); complex (level 2); moderate (level 3), and basic (level 4). In general, level 1 facilities and some level 2 facilities represent large urban, academic teaching medical centers.

Clinical outcomes. SMR and OMELOS were obtained from the Inpatient Evaluation Center (IPEC) for fiscal year 2009 (12). Because this is a restricted website, the data for publication were obtained by a Freedom of Information Act (FOIA) request. SMR was calculated as the observed number of patients admitted to an acute care ward or ICU who died within 30 days divided by the number of predicted deaths for the acute care ward or ICU. Admissions to a VA nursing home, rehabilitation or psychiatry ward were excluded. Observed minus expected length of stay (OMELOS) was determined by subtracting the observed length of stay minus the predicted length of stay for the acute care ward or ICU from the risk adjusted length of stay model (12). Readmission rate was expressed as a percentage of patients readmitted within 30 days.

Financial data. Financial data were obtained from the VSSC menu formerly known as the klf menu.  Because this is also a restricted website, the data for publication were also obtained by a Freedom of Information Act (FOIA) request. In each case, data were expressed as costs per unique in order to compare expenditures between groups. MD and RN costs reported on the VSSC menu were not expressed per unique but only per full time equivalent employee (FTE). To convert to MD or RN cost per unique, the costs per FTE were converted to MD or RN cost per unique as below (MD illustrated):

Similarly, all other direct care personnel costs/unique was calculated as below:

Direct care costs were calculated as the sum of drug, lab, x-ray, MD, RN, and other direct care personnel costs. Non-direct care costs were calculated as total costs minus direct care costs.

Correlation of Outcomes with Costs. Pearson correlation coefficient was used to determine the relationship between outcomes and costs. Significance was defined as p<0.05.

Results

Costs: The average cost per unique was $6058. Direct care costs accounted for 53% of the costs while non-direct costs accounted for 47% of the costs (Table 1 and Appendix 1).

Table 1. Average and percent of total costs/unique.

Hospital level. Data were available from 105 VA medical centers with acute care wards and 98 with ICUs. Consistent with previous data showing improved outcomes with larger medical centers, hospitals with higher levels of care (i.e. hospitals with lower level numbers) had decreased ICU SMR (Table 2). Higher levels of care also correlated with decreased ICU OMELOS and readmission rates (Table 2). For full data and other correlations see Appendix 1.

Table 2. Hospital level of care compared to outcomes. Lower hospital level numbers represent hospitals with higher levels of care.

 

*p<0.05

SMR. Increased total costs correlated with decreased intensive care unit (ICU) SMR (Table 3, r=-0.2779, p<0.05) but not acute care (hospital) SMR. Increased costs for lab, radiology, MD and other direct care staff costs and total direct care costs also correlated with decreased SMR for both ICU and acute care SMR (p<0.05, all comparisons). However, drug costs did not correlate with either acute care or ICU SMR. Increased RN costs correlated with improved ICU SMR but not acute care SMR. For full data and other correlations see Appendix 1.

Table 3. Correlation of SMR and costs.

*p<0.05

OMELOS. There was no correlation between SMR and OMELOS for either acute care (r= -0.0670) or ICU (r= -0.1553). There was no correlation between acute care or ICU OMELOS and clinical expenditures other than higher MD costs positively correlated with increased OMELOS (Table 4, p<0.05, both comparisons).

Table 4. Correlation of OMELOS and costs

*p<0.05

Readmission rate. There was no correlation between readmission rates and acute care SMR (r= -0.0074) or ICU SMR (r= 0.0463).Total and all clinical care costs directly correlated with readmission rates while non-direct clinical care costs did not (Table 5).

Table 5.Correlation of readmission rates and costs.

*p<0.05

Discussion

The data in this manuscript demonstrate that most clinical costs are correlated with a decreased or improved SMR Only MD costs correlate with OMELOS but all clinical costs directly correlate with increased readmission rates. However, non-direct care costs do not correlate with any clinical outcome.

A number of studies have examined nurse staffing.  Increased nurse staffing levels are associated with improved outcomes, including mortality in some studies (1-5). The data in the present manuscript confirm those observations in the ICU but not for acute care (hospital). However, these data also demonstrate that higher lab, X-ray and MD costs also correlate with improved SMR. Interestingly, the strongest correlation with both acute care and ICU mortality was MD costs. We speculate that these observations are potentially explained that with rare exception, nearly all physicians see patients in the VA system. The same is not true for nurses. A number of nurses are employed in non-patient care roles such as administration, billing, quality assurance, etc. It is unclear to what extent nurses without patient care responsibilities were included in the RN costs.

These data support that readmission rates are associated with higher costs but do not support that increased OMELOS is associated with higher costs implying that efforts to decrease OMELOS may be largely wasted since they do not correlate with costs or mortality. It is unclear whether the increased costs with readmissions are because readmissions lead to higher costs or the higher clinical care costs cause the higher readmissions, although the former seem more likely.

These data are derived from the VA, the Nation’s largest healthcare system. The VA system has unique features and actual amounts spent on direct and non-direct clinical care may differ from other healthcare systems. There may be aspects of administrative costs that are unique to the VA system, although it is very likely there is applicability of these findings to other healthcare systems. 

A major weakness of these data is that it is self reported. Data reported to central reporting agencies may be confusing with overlapping cost centers. Furthermore, personnel or other costs might be assigned to inappropriate cost centers in order to meet certain administrative goals. For example, 5 nurses and 1 PhD scientist were assigned to the pulmonary clinic at the Phoenix VA Medical Center while none performed any services in that clinic (Robbins RA, unpublished observations). These types of errors could lead to inaccurate or inappropriate conclusions after data analysis.

A second weakness is that the observational data reported in this manuscript are analyzed by correlation.  Correlation of decreased clinical care spending with increased mortality does not necessarily imply causation (13). For example, clinical costs are increased with readmission rates. However, readmission rates may also be higher with sicker patients who require readmission more frequently. The increased costs could simply represent the higher costs of caring for sicker patients.

A third weakness is that non-direct care costs are poorly defined by these databases. These costs likely include such essential services as support service personnel, building maintenance, food preparation, utilities, etc. but also include administrative costs. Which of these services account for variation in non-direct clinical costs is unknown. However, administrative efficiency is known to be poor and declining in the US, with increasing numbers of administrators leading to increasing administrative costs (14).

A number of strategies to control medical expenditures have been initiated, although these have almost invariably been directed at clinical costs. Programs designed to limit clinical expenditures such as utilization reviews of lab or X-ray expenditures or decreasing clinical MD or RN personnel have become frequent.  Even if costs are reduced, the present data imply that these programs may adversely affect patient mortality, suggesting that caution in limiting clinical expenses are needed. In addition, programs have been initiated to reduce both OMELOS and readmission rates. Since neither costs nor mortality correlate with OMELOS, these data imply that programs focusing on reducing OMELOS are unlikely to be successful in improving mortality or in reducing costs.

Non-direct patient care costs accounted for nearly half of the total healthcare costs in this study. It is unknown which cost centers account for variability in non-clinical areas. Since non-direct care costs do not correlate with outcomes, focus on administrative efficiency could be a reasonable performance measure to reduce costs. Such a performance measure has been developed by the Inpatient and Evaluation Center at the VA (15). This or similar measures should be available to policymakers to provide better care at lower costs and to incentivize administrators to adopt practices that lead to increased efficiency.

References

  1. Needleman J, Buerhaus P, Mattke S, Stewart M, Zelevinsky K. Nurse-staffing levels and the quality of care in hospitals. N Engl J Med 2002;346:1715-22.
  2. Aiken LH, Clarke SP, Sloane DM, Sochalski J, Silber JH. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA 2002;288:1987-93.
  3. Aiken LH, Cimiotti JP, Sloane DM, Smith HL, Flynn L, Neff DF. Effects of nurse staffing and nurse education on patient deaths in hospitals with different nurse work environments. Med Care 2011;49:1047-53.
  4. Diya L, Van den Heede K, Sermeus W, Lesaffre E. The relationship between in-hospital mortality, readmission into the intensive care nursing unit and/or operating theatre and nurse staffing levels. J Adv Nurs 2011 Aug 25. doi: 10.1111/j.1365-2648.2011.05812.x. [Epub ahead of print]
  5. Cho SH, Hwang JH, Kim J. Nurse staffing and patient mortality in intensive care units. Nurs Res 2008;57:322-30.
  6. Volpp KG, Rosen AK, Rosenbaum PR, Romano PS, Even-Shoshan O, Canamucio A, Bellini L, Behringer T, Silber JH. Mortality among patients in VA hospitals in the first 2 years following ACGME resident duty hour reform. JAMA 2007;298:984-92.
  7. Lagu T, Rothberg MB, Nathanson BH, Pekow PS, Steingrub JS, Lindenauer PK. The relationship between hospital spending and mortality in patients with sepsis. Arch Intern Med 2011;171:292-9.
  8. Cleverley WO, Cleverley JO. Is there a cost associated with higher quality? Healthc Financ Manage 2011;65:96-102.
  9. Chen LM, Jha AK, Guterman S, Ridgway AB, Orav EJ, Epstein AM. Hospital cost of care, quality of care, and readmission rates: penny wise and pound foolish? Arch Intern Med 2010;170:340-6.
  10. Render ML, Almenoff P. The veterans health affairs experience in measuring and reporting inpatient mortality. In Mortality Measurement. February 2009. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/qual/mortality/VAMort.htm
  11. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med;360:1418-28.
  12. Render ML, Kim HM, Deddens J, Sivaganesin S, Welsh DE, Bickel K, Freyberg R, Timmons S, Johnston J, Connors AF Jr, Wagner D, Hofer TP. Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure. Crit Care Med 2005;33:930-9.
  13. Aldrich J. Correlations genuine and spurious in Pearson and Yule. Statistical Science 1995;10:364-76.
  14. Woolhandler S, Campbell T, Himmelstein DU. Health care administration in the United States and Canada: micromanagement, macro costs. Int J Health Serv. 2004;34:65-78.
  15. Gao J, Moran E, Almenoff PL, Render ML, Campbell J, Jha AK. Variations in efficiency and the relationship to quality of care in the Veterans health system. Health Aff (Millwood) 2011;30:655-63.

Click here for Appendix 1.

Reference as: Robbins RA, Gerkin R, Singarajah CU. Correlation between patient outcomes and clinical costs in the va healthcare system. Southwest J Pulm Crit Care 2012;4:94-100. (Click here for a PDF version)