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General Medicine

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Tacrolimus-Associated Diabetic Ketoacidosis: A Case Report and Literature 
Nursing Magnet Hospitals Have Better CMS Hospital Compare Ratings
Publish or Perish: Tools for Survival
Is Quality of Healthcare Improving in the US?
Survey Shows Support for the Hospital Executive Compensation Act
The Disruptive Administrator: Tread with Care
A Qualitative Systematic Review of the Professionalization of the 
   Vice Chair for Education
Nurse Practitioners' Substitution for Physicians
National Health Expenditures: The Past, Present, Future and Solutions
Credibility and (Dis)Use of Feedback to Inform Teaching : A Qualitative
   Case Study of Physician-Faculty Perspectives
Special Article: Physician Burnout-The Experience of Three Physicians
Brief Review: Dangers of the Electronic Medical Record
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
Comparisons between Medicare Mortality, Readmission and 
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.



Tacrolimus-Associated Diabetic Ketoacidosis: A Case Report and Literature Review

Stella Pak, MD1

Megan Hirschbeck, BS2

Damian Valencia, MD1

Victor Valencia, BS3

Yusuf Askaroglu, BS2

Dexter Nye, BS2

Adam Fershko, MD2


1Departments of Medicine, Kettering Medical Center, Kettering, Ohio USA

2Department of Medicine, Boonshoft School of Medicine, Dayton, Ohio USA 

3Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois USA



Post-transplant diabetes mellitus is a well-established adverse effect of calcineurin inhibitors, such as tacrolimus and cyclosporine. Failure to identify and manage this side effect in a timely manner could lead to life-threatening complications like diabetic ketoacidosis (DKA). To the best of our knowledge, this is the seventh published case of an uncommon but severe, and potentially fatal, adverse effect from tacrolimus after renal transplantation. The purpose of this case report is to add to the scant body of literature on tacrolimus-induced diabetes following renal transplantation.


New-onset diabetes mellitus after transplantation (NODAT) is a well-established adverse effect of calcineurin inhibitors, such as tacrolimus and cyclosporine. NODAT has been reported to occur in 13.4% of patients after solid organ transplantation, with a higher incidence in patients receiving tacrolimus than cyclosporine, 16.6% and 9.8% respectively (1). Failure to identify and manage this side effect in a timely manner could lead to life-threatening complications like diabetic ketoacidosis (DKA). Currently, only seven reported cases, including this report, of NODAT with DKA exist in the English literature. This case report describes a patient who developed tacrolimus-induced diabetic ketoacidosis three months after receiving renal transplantation.

Case Description

The patient is a 44-year-old Caucasian male with no past medical history of diabetes mellitus who presented with diabetic ketoacidosis three months after receiving a deceased-donor kidney transplant for end stage renal disease secondary to autosomal dominant polycystic kidney disease. The patient’s immunosuppressive regimen included tacrolimus, mycophenolate and low dose prednisone (5 mg daily). The patient initially presented with complaints of nausea and polyuria. He did not have a family history of diabetes mellitus. Physical examination was unremarkable except for a body mass index (BMI) of 27 kg/m2. Laboratory work-up revealed hyperglycemia with a glucose of 493; an anion gap metabolic acidosis with pH at 7.32 and bicarbonate at 19; significant ketosis; and ketonuria. Glycated hemoglobin (hemoglobin A1c) was 9.8 % compared to 4.8% thirty days post-transplant. Tacrolimus trough level was in the therapeutic range. Glutamic acid decarboxylase (GAD-65) autoantibodies were negative. The patient received intravenous fluids, a bolus of intravenous insulin followed by a continuous insulin infusion which was soon switched to subcutaneous insulin. Upon resolution of the patients DKA, the total daily maintenance insulin requirements were approximately 40 units. The patient received diabetic education and was discharged home.


At the time of writing this report, there were only six reported cases of NODAT following the use of cyclosporine inhibitors such as tacrolimus which are summarized in table 1 (2-7).

Table 1. Summary of the seven reported cases focused on clinical presentation and management.

BMI: body-mass-index; M: male; F: female; BM: bone marrow; TAC: tacrolimus; MPL: methylprednisolone; PDL: prednisolone; PDN: prednisone; MPS: mycophenolate sodium; MMF: mycophenolate mofetil; AZT: azathioprine; CYC: cyclosporine; RG: random glucose; FG: fasting glucose; Yr: year; Mo: month; Wk: week; NA: not available; DM: diabetes mellitus; Tx: therapy

Five of the now seven reported cases detail the development of diabetic ketoacidosis at least six months after renal transplantation (2-5, 7). In contrast, our case and the case detailed by Dr. Tuğcu and his colleagues describe the presentation of DKA and new onset diabetes within three months of transplantation (6).

Maintenance immunosuppressive therapy is essential to prevent organ rejection in renal transplant recipients. Calcineurin inhibitors play an integral role in most immunosuppressive regimens, with tacrolimus being the preferred agent over cyclosporine, as several studies show lower incidence of acute rejection with its use (1). Both calcineurin inhibitors are known to cause toxicity to pancreatic islet beta cells and may also directly affect transcriptional regulation of insulin expression (5). Evidence suggests that tacrolimus causes greater incidence of severe swelling-vacuolization, endoplasmic reticulum stress and apoptosis of pancreatic islet beta cells when compared to cyclosporine (8). Tacrolimus associated diabetogenic effects threaten the health and longevity of the allograft by predisposing the recipients to microvascular and macrovascular diabetic complications which subsequently reduce allograft survival.

The development of diabetes mellitus Type 1 with ketoacidosis in patients on therapeutic tacrolimus with no risk factors for diabetes highlights the need for alternative immunosuppressive agents which won’t compromise long-term survival of the patients’ allograft. This case report highlights the importance of regular fasting blood glucose monitoring in patients on a tacrolimus-regimen for immunosuppression in order to prevent the life-threatening complication of diabetic ketoacidosis and subsequent allograft rejection in the setting of uncontrolled diabetes mellitus.

Post-transplant diabetes mellitus is associated with increased mortality, by approximately 10 %, among renal transplant patients. The reduction in survival rate due to post-transplant diabetes mellitus is largely due to cardiovascular disease, such as coronary artery disease and congestive heart failure (9). Given the negative impact of NODAT in the survival of renal transplant patients, preventive efforts should be made to minimize risk factors. Known risk factors for NODAT include obesity, hepatitis C, African American race, Hispanic ethnicity, family history of diabetes mellitus, use of calcineurin inhibitor and/or corticosteroid. Early identification of patients at high risk for NODAT would help tailor immunotherapeutic suppressant regimen and to aggressively manage modifiable risk factors for NODAT (10, 11). The International Diabetes Federation (IDF) recommends proactive prescreening of all post-transplant patients for NODAT, with measurement of fast plasma glucose at least once per week for first 4 weeks post-transplant. Afterwards, post-transplant patients should have fasting plasma glucose test at 3, 6, 12 months, at 1-year intervals thereafter. Glycated hemoglobin (Hemoglobin A1C) is recommended to be check at 3 months following the transplant procedure (12).

Calcineurin inhibitors, including tacrolimus and cyclosporine, inhibit calcineurin in β-cells of the pancreas. Inhibition of calcineurin indirectly suppresses expression of genes involved in insulin production. In particular, adverse glycemic effect occurs with a greater incidence with tacrolimus than cyclosporine. This is thought to be mostly due to tacrolimus-induced changes in the level of β-cell enriched transcription factors, forkhead box protein O1 (FoxO1) and v-maf musculoaponeurotic fibrosarcoma oncogene homolog A (MafA). Tacrolimus selectively promotes nuclear translocation of FoxO1 and cytoplasmic location of MafA. These modulations of transcript factors then cause β-cell dysfunction, attributing to the development of NODAT (13).


Considering the potentially devastating complication of allograft compromise due to undiagnosed NODAT, it is imperative that clinicians monitor patients for signs of impaired glucose metabolism, specifically those who are treated with tacrolimus.


  1. Heisel O, Heisel R, Balshaw R, Keown P. New onset diabetes mellitus in patients receiving calcineurin inhibitors: a systematic review and meta-analysis. Am J Transplant. 2004;4:583-95. [CrossRef] [PubMed]
  2. Cho YM, Park KS, Jung HS, Kim YS, Kim SY, Lee HK. A case showing complete insulin independence after severe diabetic ketoacidosis associated with tacrolimus treatment. Diabetes Care. 2002;25:1664. [CrossRef] [PubMed]
  3. Ersoy A, Ersoy C, Tekce H, Yavascaoglu I, Dilek K. Diabetic ketoacidosis following development of de novo diabetes in renal transplant recipient associated with tacrolimus. Transplant Proc. 2004;36:1407-10. [CrossRef] [PubMed]
  4. Masood MQ, Rabbani M, Jafri W, Habib M, Saleem T. Diabetic ketoacidosis associated with tacrolimus in solid organ transplant recipients. J Pak Med Assoc. 2011;61:288-90. [PubMed]
  5. Toyonaga T, Kondo T, Miyamura N, et al. Sudden onset of diabetes with ketoacidosis in a patient treated with FK506/tacrolimus. Diabetes Res Clin Pract. 2002;56:13-8. [CrossRef] [PubMed
  6. Tuğcu M, Kasapoglu U, Boynuegri B, et al. Tacrolimus-Induced Diabetic Ketoacidosis and Effect of Switching to Everolimus: A Case Report. Transplant Proc. 2015;47:1528-30. [CrossRef] [PubMed]
  7. Yoshida EM, Buczkowski AK, Sirrs SM, et al. Post-transplant diabetic ketoacidosis--a possible consequence of immunosuppression with calcineurin inhibiting agents: a case series. Transpl Int. 2000;13:69-72. [CrossRef] [PubMed]
  8. Rangel EB. Tacrolimus in pancreas transplant: a focus on toxicity, diabetogenic effect and drug-drug interactions. Expert Opin Drug Metab Toxicol. 2014;10: 1585-605. [CrossRef] [PubMed]
  9. Shivaswamy V, Boerner B, Larsen J. Post-transplant diabetes mellitus: causes, treatment, and impact on outcomes. Endocr Rev. 2016;37(1):37-61. [CrossRef] [PubMed]
  10. Kasiske BL, Snyder JJ, Gilbertson D, Matus AJ. Diabetes mellitus after kidney transplantation in the United States. Am J Transplant. 2003;3:178-85. [CrossRef] [PubMed]
  11. Karavelioglu D, Baysal C, Ozdemir N et al. Impact of HCV infection on the development of post transplantation diabetes mellitus in renal allograft recipients. Transplantation Proc. 2000;32;(3):561-2. [CrossRef] [PubMed]
  12. Wilkinson A, Davidson J, Dotta F et al. Guidelines for the treatment and management of new onset of diabetes after transplantation. Clin Transplant. 2005;19:291-8. [CrossRef] [PubMed]
  13. Trianes J, Rodriguez-Rodriguez AE, Brito-Cassilas Y, et al. Deciphering tacrolimus-induced toxicity in pancreatic β cells. Am J Transplant. 2017;17: 2829-40. [CrossRef] [PubMed] 

Cite as: Pak S, Hirschbeck M, Valencia D, Valencia V, Askaroglu Y, Nye D, Fershko A. Tacrolimus-associated diabetic ketoacidosis: a case report and literature review. Southwest J Pulm Crit Care. 2018;16(2):103-7. doi: PDF 


Nursing Magnet Hospitals Have Better CMS Hospital Compare Ratings

Richard A. Robbins, MD

Phoenix Pulmonary and Critical Care Research and Education Foundation

Gilbert, AZ USA


Background: There has been conflicting data on whether Nursing Magnet Hospitals (NMH) provide better care.

Methods: NMH in the Southwest USA (Arizona, California, Colorado, Hawaii, Nevada, and New Mexico) were compared to hospitals not designated as NMH using the Centers for Medicare and Medicaid (CMS) hospital compare star designation.

Results: NMH had higher star ratings than non-NMH hospitals (3.34 + 0.78 vs. 2.86 + 0.83, p<0.001). The hospitals were mostly large, urban non-critical access hospitals. Academic medical centers made up a disproportionately large portion of the NMH.

Conclusions: Although NMH had higher hospital ratings, the data may favor non-critical access academic medical centers which are known to have better outcomes.


Magnet status is awarded to hospitals that meet a set of criteria designed to measure nursing quality by the American Nurses' Credentialing Center (ANCC), a part of the American Nurses Association (ANA). The Magnet designation program was based on a 1983 ANA survey of 163 hospitals deriving its key principles from the hospitals that had the best nursing performance. The prime intention was to help hospitals and healthcare facilities attract and retain top nursing talent.

There is no consensus whether Magnet status has an impact on nurse retention or on clinical outcomes. Kelly et al. (1) found that NMH hospitals provide better work environments and a more highly educated nursing workforce than non-NMH. In contrast, Trinkoff et al. (2) found no significant difference in working conditions between NHM and non-NMH. To further confuse the picture, Goode et al. (3) reported that NMH generally had poorer outcomes.

The Centers for Medicare and Medicaid Services (CMS) has developed star ratings in an attempt to measure quality of care (4). The ratings are based on five broad categories: 1. Outcomes; 2. Intermediate Outcomes; 3. Patient Experience; 4. Access; and 5. Process. Outcomes and intermediate outcomes are weighted three times as much as process measures, and patient experience and access measures are weighted 1.5 times as much as process measures. The ratings are from 1-5 stars with higher numbers of stars indicating a higher quality rating.

This study compares the CMS star ratings between NMH and non-NMH in the Southwest USA (Arizona, California, Colorado, Hawaii, Nevada and New Mexico). The results demonstrate that NMH have higher CMS star ratings. However, the NMH have characteristics which have been previously associated with higher quality of care using some measures.


Nursing Magnet Hospitals

NMH were identified from The American Nurses Credentialing Center website (5).

CMS Star Ratings

Star ratings were obtained from the CMS website (4).


Only when data was available for both NMH and CMS star ratings were the hospitals included. Data was expressed as mean + standard deviation.  NMH and non-NMH were compared using Student’s t test. Significance was defined as p<0.05.


Hospital Characteristics

There were 44 NMH and 415 non-NMH hospitals in the data (see Appendix). California had the most hospitals (287) and the most NMH (28). Arizona had 8 NMH, Colorado 7 and Hawaii 1. Nevada and New Mexico had none. All the NMH were acute care hospitals located in major metropolitan areas. Most were larger hospitals. None were designated critical access hospitals by CMS. Eleven of the NMH were the primary teaching hospitals for medical schools. Many of the others had affiliated teaching programs.

CMS Star Ratings

The CMS star ratings were higher for NMH than non NMH (3.34 + 0.78 vs. 2.86 + 0.83, p<0.001, Figure 1).

Figure 1. CMS star ratings for Nurse Magnet Hospitals (NMH) and non-NMH (p<0.001).


The present study shows that for hospitals in the Southwest, NMH had higher CMS star ratings than non-NMH. This is consistent with better levels of care in NMH than non-NMH. However, the NMH were large, urban, non-critical access medical centers which were disproportionately academic medical centers. Previous studies have shown that these hospitals have better outcomes (6,7).

There seems to be little consensus in the literature regarding patient outcomes in NMH. A 2010 study concluded that non-NMH actually had better patient outcomes than NMH (3). Similarly, studies published early in this decade suggested little difference in outcomes (1,2). In contrast, a more recent study suggested improvements in patient outcomes in NMH (8). The present study supports the concept that NMH status might be a marker for better patient outcomes.

Achieving NMH status is expensive. Hospitals pay about $2 million for initial NMH certification, and pay nearly the same amount for re-certification every 4 years. It seems unlikely that small rural hospitals could afford the fee to achieve and maintain NMH regardless of their quality of care. Therefore, the NMH would be expected to be larger, urban medical centers which were the results found in the present study.

Despite there being no direct link of NMH to reimbursement, a study by the Robert Wood Johnson Foundation suggests that achieving NMH status increased hospital revenue (9). On average, NMH received an adjusted net increase in inpatient income of about $104 to $127 per discharge after earning Magnet status, amounting to about $1.2 million in revenue each year. The reason(s) for the improvement in hospital fiscal status are unclear.

Measuring quality of care is quite complex. The CMS star ratings are an attempt to summarize the quality of care using 5 broad categories: 1. Outcomes; 2. Intermediate Outcomes; 3. Patient Experience; 4. Access; and 5. Process. There are up to 32 measures in each category. Outcomes, patient experience and access seem relatively straight-forward. An example of a secondary outcome is control of blood pressure because of its link to outcomes. Examples of process measures include colorectal cancer screening, annual flu shot and monitoring physical activity. To further complicate the CMS ratings, each category is weighted.

It is possible that the CMS star ratings might miss or under weigh a key element in quality of care. For example, Needleman et al. (10) has emphasized that increased registered nurse staffing reduces hospital mortality. However, a 2011 study concluded that NMH had less total staff and a lower RN skill mix compared with non-NMH hospitals contributing to poorer outcomes (3).

The present study supports the concept that achieving NMH status is associated with better care as defined by CMS. However, given the complexities of measuring quality of care it is unclear whether this represents a marker of better hospitals or if the process of achieving NMH leads to better care.


  1. Kelly LA, McHugh MD, Aiken LH. Nurse outcomes in Magnet® and non-Magnet hospitals. J Nurs Adm. 2012 Oct;42(10 Suppl):S44-9. [PubMed]
  2. Trinkoff AM, Johantgen M, Storr CL, Han K, Liang Y, Gurses AP, Hopkinson S. A comparison of working conditions among nurses in Magnet and non-Magnet hospitals. J Nurs Adm. 2010 Jul-Aug;40(7-8):309-15. [CrossRef] [PubMed]
  3. Goode CJ, Blegen MA, Park SH, Vaughn T, Spetz J. Comparison of patient outcomes in Magnet® and non-Magnet hospitals. J Nurs Adm. 2011 Dec;41(12):517-23. [CrossRef] [PubMed]
  4. Centers for Medicare and Medicaid. 2017 star ratings. Available at: (accessed 10/15/17).
  5. The American Nurses Credentialing Center. ANCC List of Magnet® Recognized Hospitals. Available at: (accessed 10/15/17).
  6. Burke LG, Frakt AB, Khullar D, Orav EJ, Jha AK. Association Between Teaching Status and Mortality in US Hospitals. JAMA. 2017 May 23;317(20):2105-13. [CrossRef] [PubMed]
  7. Joynt KE, Harris Y, Orav EJ, Jha AK. Quality of care and patient outcomes in critical access rural hospitals. JAMA. 2011 Jul 6;306(1):45-52. [CrossRef] [PubMed]
  8. Friese CR, Xia R, Ghaferi A, Birkmeyer JD, Banerjee M. Hospitals in 'Magnet' program show better patient outcomes on mortality measures compared to non-'Magnet' hospitals. Health Aff (Millwood). 2015 Jun;34(6):986-92. [CrossRef] [PubMed]
  9. Jayawardhana J, Welton JM, Lindrooth RC. Is there a business case for magnet hospitals? Estimates of the cost and revenue implications of becoming a magnet. Med Care. 2014 May;52(5):400-6. [CrossRef] [PubMed]
  10. Needleman J, Buerhaus P, Pankratz VS, Leibson CL, Stevens SR, Harris M. Nurse staffing and inpatient hospital mortality. N Engl J Med. 2011 Mar 17;364(11):1037-45.[CrossRef] [PubMed]

Cite as: Robbins RA. Nursing magnet hospitals have better CMS hospital compare ratings. Southwest J Pulm Crit Care. 2017;15(5):209-13. doi: PDF 


Publish or Perish: Tools for Survival

Stuart F. Quan, M.D.1

Jonathan F. Borus, M.D.2


1Division of Sleep and Circadian Disorders and 2Department of Psychiatry

Brigham and Women’s Hospital

Harvard Medical School

Boston, MA USA


(Editor's Note: A downloadable PowerPoint presentation accompanies this article and be accessed by clicking on the following link "Publish or Perish: Tools for Suvival". It is 20 Mb and may take some time to download).

Success in one’s chosen profession is often predicated upon meeting a profession-wide standard of excellence or productivity. In the corporate world, the metric might be sales volume and in clinical medicine it may be patient satisfaction and/or number of patients seen. In academic medicine, including the fields of Pulmonary and Critical Care Medicine, the “coin of the realm” is demonstrable written scholarship. In large part, this is determined by the number and quality of publications in scientific journals. Unfortunately, the skills required to navigate the complexities of how to publish in the scientific literature rarely are taught in either medical school or postgraduate training. To assist the inexperienced academic physician or scientist, the Writing for Scholarship Interest Group of the Harvard Medical School Academy recently published “A Writer’s Toolkit” (1). This comprehensive monograph provides valuable information on all phases of the writing process ranging from conceptualization of a manuscript to understanding of the publication process itself. In today’s society, however, there are alternative methods of disseminating knowledge that may be better received by some learners than traditional prose. Examples include videos, podcasts and online interactive courses.

In order to provide a complementary method of presenting some of the information contained in “A Writer’s Toolkit” for more active learners, we have developed a self-paced interactive learning module to help young authors better understand the submission, review, and response to reviews stages of the publishing process. The module entitled “Publish or Perish: Tools for Survival” is downloadable from this journal’s website. We believe that providing a way for self-learners to better understand these processes will help such inexperienced authors more successfully get published and therefore share their work with others in the field.


  1. Pories S. Bard T, Bell S et al. A Writer’s Toolkit. MedEdPORTAL, Association of American Medical Colleges; 2012. Available from:

Cite as: Quan SF, Borus JF. Publish or perish: tools for survival. Southwest J Pulm Crit Care. 2017;14(2):67. doi: PDF 


Is Quality of Healthcare Improving in the US?

Richard A. Robbins, MD

Phoenix Pulmonary and Critical Care Research and Education Foundation

Gilbert, AZ USA



Politicians and healthcare administrators have touted that under their leadership enormous strides have been made in the quality of healthcare. However, the question of how to measure quality remains ambiguous. To demonstrate improved quality that is meaningful to patients, outcomes such as life expectancy, mortality, and patient satisfaction must be validly and reliably measured. Dramatic improvements made in many of these patient outcomes through the twentieth century have not been sustained through the twenty-first. Most studies have shown no, or only modest improvements in the past several years, and at a considerable increase in cost. These data suggest that the rate of healthcare improvement is slowing and that many of the quality improvements touted have not been associated with improved outcomes.

Surrogate Markers

The most common measures of quality of healthcare come from Donabedian in 1966 (1). He identified two major foci for the measuring quality of care-outcome and process. Outcome referred to the condition of the patient and the effectiveness of healthcare including traditional outcome measures such as morbidity, mortality, length of stay, readmission, etc. Process of care represented an alternative approach which examined the process of care itself rather than its outcomes.

Beginning in the 1970’s the Joint Commission began to address healthcare quality by requiring hospitals to perform medical audits. However, the Joint Commission soon realized that the audit was “tedious, costly and nonproductive” (2). Efforts to meet audit requirements were too frequently “a matter of paper compliance, with heavy emphasis on data collection and few results that can be used for follow-up activities. In the shuffle of paperwork, hospitals often lost sight of the purpose of the evaluation study and, most important, whether change or improvement occurred as a result of audit”. Furthermore, survey findings and research indicated that audits had not resulted in improved patient care and clinical performance (2).

In response to the ineffectiveness of the audit and the call to improve healthcare, the Joint Commission introduced new quality assurance standards in 1980 which emphasized measurable improvement in process of care rather than outcomes. This approach proved popular with both regulatory agencies and healthcare investigators since it was easier and quicker to show improvement in process of care surrogate markers than outcomes.

Although there are many examples of the misapplication of these surrogate markers, one recent example of note is ventilator-associated pneumonia (VAP), a diagnosis without a clear definition. VAP guidelines issued by the Institute for Healthcare Improvement include elevation of the head of the bed, daily sedation vacation, daily readiness to wean or extubate, daily spontaneous breathing trial, peptic ulcer disease prophylaxis, and deep venous thrombosis prophylaxis. As early as 2011, the evidence basis of these guidelines was questioned (3). Furthermore, compliance with the guidelines had no influence on the incidence of VAP or inpatient mortality (3). Nevertheless, relying on self-reported hospital data the CDC published data touting declines in VAP rates of 71% and 62% in medical and surgical intensive care units, respectively, between 2006 and 2012 (4,5). However, Metersky and colleagues (6) reviewed Medicare Patient Safety Monitoring System (MPSMS) data on 86,000 critically ill patients between 2005 and 2013 and report that VAP rates remain unchanged since 2005.

Hospital Value-Based Purchasing (HVBP)

CMS’ own data might be interpreted as showing no improvement in quality. About 200 fewer hospitals will see bonuses from the Centers for Medicare and Medicaid Services (CMS) under the hospital value-based purchasing (HVBP) program in 2017 than last year (7). The program affects some 3,000 hospitals and compares hospitals to other hospitals and its own performance over time.

The reduction in payments are “somewhat concerning,” according to Francois de Brantes, executive director of the Health Care Incentives Improvement Institute (7). One reason given was fewer hospitals were being rewarded, but another was hospitals' lack of movement in rankings. The HVBP contains inherent design flaws according to de Brantes. As a "tournament-style" program in which hospitals are stacked up against each other, they don't know how they'll perform until the very end of the tournament. "It's not as if you have a specific target," he said. "You could meet that target, but if everyone meets that target, you're still in the middle of the pack."

Although de Brantes point is well taken, another explanation might be that HVBP might reflect a declining performance in healthcare. If the HVBP program is to reward quality of care, fewer hospitals being rewarded logically indicates poorer care. As noted above, CMS will likely be quick to point out that they have established an ever-increasing litany of "quality" measures self-reported by the hospitals that show increasing compliance with these measures (8). However, the lack of improvement in patient outcomes (see below) suggests that completion of these has little meaningful effect.

Life Expectancy

Although life expectancy for the Medicare age group is improving, the increase likely reflects a long-term improvement in life expectancy and may be slowing over the past few years (Figure 1) (9). Since 2005, life expectancy at birth in the U.S. has increased by only 1 year (10).

Figure 1. Life expectancy past age 65 by year.

The reason(s) for the declining improvement in life expectancy in the twenty-first century compared to the dramatic improvements in the twentieth are unclear but likely multifactorial. However, one possible contributing factor to a slowing improvement in mortality is a declining or flattening rate of improvement in healthcare.

Inpatient Mortality

Figueroa et al. (11) examined the association between HVBP and patient mortality in 2,430,618 patients admitted to US hospitals from 2008 through 2013. Main outcome measures were 30-day risk adjusted mortality for acute myocardial infarction, heart failure, and pneumonia using a patient level linear spline analysis to examine the association between the introduction of the HVBP program and 30-day mortality. Non-incentivized, medical conditions were the comparators. The difference in the mortality trends between the two groups was small and non-significant (difference in difference in trends −0.03% point difference for each quarter, 95% confidence interval −0.08% to 0.13%-point difference, p=0.35). In no subgroups of hospitals was HVBP associated with better outcomes, including poor performers at baseline.

Consistent with Figueroa’s data, inpatient mortality trends declined only modestly from 2000 to 2010 (Figure 2) (12).

Figure 2. Number of inpatient deaths 2000-10.

Although the decline was significant, the significance appears to be mostly explained by a greater that expected drop in 2010 and may not represent a real ongoing decrease. Consistent with the modest improvements seen in overall inpatient mortality, disease-specific mortality rates for stroke, acute myocardial infarction (AMI), pneumonia and congestive heart failure (CHF) all declined from 2002-12. However, the trend appears to have slowed since 2007 especially for CHF and pneumonia (Figure 3).

Figure 3. Inpatient mortality rates for stroke, acute myocardial infarction (AMI), pneumonia and congestive heart failure (CHF) 2002-12.

Consistent with the trend of slowing improvement, mortality rates for these four conditions declined at −0.13% for each quarter during from 2008 until Q2 2011 but only −0.03% from Q3 2011 until the end of 2013 (12).

Patient Ratings of Healthcare

CMS has embraced the concept of patient satisfaction as a quality measure, even going so far as rating hospitals based on patient satisfaction (13). The Gallup company conducts an annual poll of Americans' ratings of their healthcare (14). In general, these have not improved and may have actually declined in the past 2 years (Figure 4).

Figure 4. Americans’ rating of their healthcare.


There is little doubt that healthcare costs have risen (15). The rising cost of healthcare has been cited as a major factor in Americans’ poor rating of their healthcare. The trend appears to be one of increasing dissatisfaction with the cost of healthcare (Figure 5) (16).

Figure 5. Americans’ satisfaction or dissatisfaction with the cost of healthcare.


Americans have enjoyed remarkable improvements in life expectancy, mortality, and satisfaction with their healthcare over the past 100 years. However, the rate of these improvements appears to have slowed despite an ever-escalating cost. Starting with a much lower life expectancy in the US, primarily due to infections disease, the dramatic effect of antibiotics and vaccines on overall mortality in the twentieth century would be difficult to duplicate. The current primary causes of mortality in the US, heart disease and cancer, are perhaps more difficult to impact in the same way. However, declining healthcare quality may explain, at least in part, the slowing improvement in healthcare.

The evidence of lack, or only modest, improvement in patient outcomes is part of a disturbing trend in quality improvement programs by healthcare regulatory agencies. Under political pressure to “improve” healthcare, these agencies have  imposed weak or non-evidence based guidelines for many common medical disorders. In the case of CMS, hospitals are required to show compliance improvement under the threat of financial penalties. Not surprisingly, hospitals show an improvement in compliance whether achieved or not (17). The regulatory agency then extrapolates this data from previous observational studies to show a decline in mortality, cost or other outcomes. However, actual measure of the outcomes is rarely performed. This difference is important because a reduction in a surrogate marker may not be associated with improved outcomes, or worse, the improvement may be fictitious. For example, many patients often die with a hospital-acquired infection. Certainly, hospital-acquired infections are associated with increased mortality. However, preventing the infections does not necessarily prevent death. For example, in patients with widely metastatic cancer, infection is a common cause of death. However, preventing or treating the infection, may do little other than delay the inevitable. A program to improve infections in these patients would likely have little effect on any meaningful patient outcomes.

There is also a trend of bundling weakly evidence-based, non-patient centered surrogate markers with legitimate performance measures (18). Under threat of financial penalties, hospitals are required to improve these surrogate markers, and not surprisingly their reports indicate they do. The organization mandating compliance with their outcomes reports that under their guidance hospitals have significantly improved healthcare saving both lives and money. However, if the outcome is meaningless or the hospital lies about their improvement, there is no overall quality improvement. There is little incentive for the parties to question the validity of the data. The organization that mandates the program would be politically embarrassed by an ineffective program and the hospital would be financially penalized for honest reporting.

Improvement begins with the establishment of measures that are truly evidence-based. Surrogate markers should only be used when improvement in that marker has been unequivocally shown to improve patient-centered outcomes. The validity of the data also needs to be independently confirmed. Those regulatory agency-demanded quality improvement programs that do not meet these criteria need to be regarded for what they are-political propaganda rather than real solutions.

The above data suggest that healthcare is improving little in what matters most, patient-centered outcomes. Those claims by regulatory agencies of improved healthcare should be regarded with skepticism unless corroborated by improvement in valid patient-centered outcomes.


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Cite as: Robbins RA. Is quality of healthcare improving in the US? Southwest J Pulm Crit Care. 2017;14(1):29-36. doi: PDF 


Survey Shows Support for the Hospital Executive Compensation Act

Richard A. Robbins, MD

Editor, SWJPCC

The Arizona Hospital Executive Compensation Act 2016 was an Arizona state proposition to limit healthcare executive pay to $450,000/year. An anonymous survey conducted by the Southwest Journal of Pulmonary and Critical Care (SWJPCC) from 8/1/16-8/22/16 on support for the proposition support and its possible effect on healthcare (Appendix 1). We obtained 52 responses of which 49 were from physicians and 3 from other healthcare workers. Eighty-three percent (43 of 52) supported the proposition and only 10% (5 of 52) felt if would make patient care worse. Thirty-five percent (18 of 52) felt it would make patient care better while the remaining 56% believed it would have no effect. All 5 of those who opposed the proposition felt it would make healthcare worse. These data suggest that in the opinion of those who answered a survey in the SWJPCC the vast majority supported a measure to limit healthcare executive pay and most felt it would have no effect on patient care or make it better.

Cite as: Robbins RA. Survey shows support for the hospital executive compensation act. Southwest J Pulm Crit Care. 2016;13:90. doi: PDF