Tag Archives: Hospitals

Declining Readmission Rates – Are They Associated with Increased Mortality?

I have always been a bit nihilistic about reducing readmission rates to hospitals.[1][2] However, I may have been overly pessimistic. A new study confirms that it is possible to reduce readmission rates by imposing financial incentives.[3] Importantly, this does not seem to have caused an increase in mortality – as might occur if hospitals were biased against re-admitting sick patients in order to avoid a financial penalty. “False null result” (type two error), do I hear you ask? Probably not, since the data are based on nearly seven million admissions. In fact, 30 day mortality rates were slightly lower among hospitals that reduced readmission rates.

— Richard Lilford, CLAHRC WM Director

References:

  1. Lilford RJ. If Not Preventable Deaths, Then What About Preventable Admissions? NIHR CLAHRC West Midlands News Blog. 6 May 2016.
  2. Lilford RJ. Unintended Consequences of Pay-For-Performance Based on Readmissions. NIHR CLAHRC West Midlands News Blog. 13 January 2017.
  3. Joynt KE, & Maddox TM. Readmissions Have Declined, and Mortality Has Not Increased. The Importance of Evaluating Unintended Consequences. JAMA. 2017; 318(3): 243-4.

Predicting Readmissions on the Basis of a Well-Known Risk of Readmission Score

A recent NIHR CLAHRC West Midlands study examined a score based on co-morbidities, hospital use before the index admission, length of stay, and rate of admission – the LACE score.[1] The findings broadly corroborate the score and previous evidence – high scores are statistically associated with risk of readmission, but predictive accuracy is low and hardly likely to improve on clinical assessment; no doctor would use such a test to identify patients. This is an inpatient study based on over 90,000 admissions. We do not want every clinical action to be codified in a score – it is a waste of time. Moreover, most readmissions are caused by a new problem.[2] So a more sensible way forward, from my point of view, would be a general index of risk of deterioration to cover patients at all points in their journey. Would the ‘frailty index’ [3] [4] serve this purpose perfectly well?

— Richard Lilford, CLAHRC WM Director

References:

  1. Damery S, Combes G. Evaluating the predictive strength of the LACE index in identifying patients at high risk of hospital readmission following an inpatient episode: a retrospective cohort study. BMJ Open. 2017; 7: e016921.
  2. Lilford RJ. Unintended Consequences of Pay-for-Performance Based on Readmissions. NIHR CLAHRC West Midlands News Blog. 13 January 2017.
  3. Lilford RJ. Future Trends in NHS. NIHR CLAHRC West Midlands News Blog.
  4. Clegg A, Bates C, Young J, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016.

Introducing Hospital IT systems – Two Cautionary Tales

The beneficial effects of mature IT systems, such as at the Brigham and Women’s Hospital,[1] Intermountain Health Care,[2] and University Hospitals Birmingham NHS Foundation Trust,[3] have been well documented. But what happens when a commercial system is popped into a busy NHS general hospital? Lots of problems according to two detailed qualitative studies from Edinburgh.[4] [5] Cresswell and colleagues document problems with both stand-alone ePrescribing systems and with multi-modular systems.[4] The former drive staff crazy with multiple log-ins and duplicate data entry. Nor does their frustration lessen with time. Neither system types (stand-alone or multi-modular) presented a comprehensive overview of the patient record. This has obvious implications for patient safety. How is a doctor expected to detect a pattern in the data if they are not presented in a coherent format? In their second paper the authors examine how staff cope with the above problems.[5] To enable them to complete their tasks ‘workarounds’ were deployed. These workarounds frequently involved recourse to paper intermediaries. Staff often became overloaded with work and often did not have the necessary clinical information at their fingertips. Some workarounds were sanctioned by the organisation, others not. What do I make of these disturbing, but thorough, pieces of research? I would say four things:

  1. Move slowly and carefully when introducing IT and never, never go for heroic ‘big bang’ solutions.
  2. Employ lots of IT specialists who can adapt systems to people – do not try to go the other way round and eschew ‘business process engineering’, the risks of which are too high – be incremental.
  3. If you do not put the doctors in charge, make sure that they feel as if they are. More seriously – take your people with you.
  4. Forget integrating primary and secondary care, and social care and community nurses, and meals on wheels and whatever else. Leave that hubristic task to your hapless successor and introduce a patient held booklet made of paper – that’s WISDAM.[6]

— Richard Lilford, CLAHRC WM Director

References:

  1. Weissman JS, Vogeli C, Fischer M, Ferris T, Kaushal R, Blumenthal B. E-prescribing Impact on Patient Safety, Use and Cost. Rockville, MD: Agency for Healthcare Research and Quality. 2007.
  2. Bohmer RMJ, Edmondson AC, Feldman L. Intermountain Health Care. Harvard Business School Case 603-066. 2002
  3. Coleman JJ, Hodson J, Brooks HL, Rosser D. Missed medication doses in hospitalised patients: a descriptive account of quality improvement measures and time series analysis. Int J Qual Health Care. 2013; 25(5): 564-72.
  4. Cresswell KM, Mozaffar H, Lee L, Williams R, Sheikh A. Safety risks associated with the lack of integration and interfacing of hospital health information technologies: a qualitative study of hospital electronic prescribing systems in England. BMJ Qual Saf. 2017; 26: 530-41.
  5. Cresswell KM, Mozaffar H, Lee L, Williams R, Sheikh A. W. Workarounds to hospital electronic prescribing systems: a qualitative study in English hospitals. BMJ Qual Saf. 2017; 26: 542-51.
  6. Lilford RJ. The WISDAM* of Rupert Fawdry. NIHR CLAHRC West Midlands News Blog. 5 September 2014.

Length of Hospital Stay

The average length of hospital stay for patients has ‘plummeted’ over the last thirty years, from 10 days in 1983 to 5 days in 2013.[1] However, the proportion of patients discharged to a nursing facility has quadrupled over this same period.[2] So, from the point of view of the patient, the stay away from home has not changed as much as it might be inferred from an uncritical analysis of inpatient stays. So, how have home-to-home times changed? This was assessed by Barnett et al.[3] on the basis of Medicare administration claims for 82 million hospitalisations over the years 2004 to 2011 inclusive.

Yes, the mean length of hospital stay declined (from 6.3 to 5.7 days), but the mean length of stay in post-acute care facilities increased from 4.8 to 6 days. Total home-to-home time increased from 11.1 to 11.7 days. This is not necessarily a bad thing, but it must be taken into account in assessing costs and benefits of care. The risk of iatrogenic harm and costs are lower in nursing facilities than hospitals. However, the article cited here does not consider the possibility that these risks and costs are not lower for the group of people in nursing facilities who would otherwise be cared for in hospital.

— Richard Lilford, CLAHRC WM Director

References:

  1. Centers for Medicare and Medicaid Services. CMS program statistics: 2013 Medicare Utilization Section. 2017.
  2. Tian W (AHRQ). An All-Payer View of Hospital Discharge to Postacute Care, 2013. Rockville, MD: Agency for Healthcare Research and Quality; 2016.
  3. Barnett ML, Grabowski DC, Mehrotra A. Home-to-Home Time – Measuring What Matters to Patients and Payers. N Engl J Med. 2017; 377: 4-6.

Payment by Results – a Null Result!

Reimbursement levels for medical care in large US hospitals are reduced by up to 2% if compliance with evidence-based clinical care standards falls below threshold levels. Does this result in improved care compared to control hospitals not exposed to the financial incentive? To find out, intervention hospitals were compared to control hospitals.[1] The ‘value based purchasing’ schemes were not introduced in a prospective experiment, and the controls (small rural hospitals) are very different in nature to those larger hospitals to whom the incentive applies. To mitigate potential bias, difference-in-difference approaches were used; hospitals were matched for previous performance; and the usual statistical adjustments were made. Adherence to appropriate clinical processes was increasing among both control and intervention hospitals before the intervention was implemented. Rates of adherence did not differ between intervention and control hospitals post-intervention. The clinical indicators related to three tracer conditions frequently used in studies of adherence to clinical standards – pneumonia, heart attack or heart failure. Patient experience measures also did not differ over intervention and controls, and while mortality was improved for pneumonia, it did not do so for the other conditions. The effect on pneumonia deaths was regarded as a chance finding (alpha error), given the null result on mediating variables (i.e. clinical process variables). Arguably these results were null because the incentive was low (only 2% of total reimbursement) and distributed over a large number of outcomes. Alternatively, doctors are largely intrinsically motivated and do not need financial incentives to moderate their performance. We will pick up on this issue in our next News Blog.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Ryan AM, Krinsky S, Maurer KA, Dimick JB. Changes in Hospital Quality Associated with Hospital Value-Based Purchasing. N Engl J Med. 2017; 376: 2358-66.

The Increasing Codification and Transparency of Hospital Practice

Ah the halcyon days, when at the age of 26 I was the most senior obstetrician on site in a huge high-risk hospital. How times have changed. Now a fully accredited specialist must be on hand, she must follow check-lists, and cognitive aids will try to pre-empt errors.[1] And now we hear that she will be under video-surveillance for a substantial portion of her working life.[2] Of course all of this is a good thing – codification reduces error in all industries. And we must also realise that codification does not mean that the need for judgement is vitiated, or that medicine cannot still be fun and even heroic, as we have argued before.[3] And transparency is also no bad thing; as it punishes those who transgress so it exonerates the many who are falsely accused.

— Richard Lilford, CLAHRC WM Director

References:

  1. Merry AF, Mitchell SJ. Advancing Patient Safety Through the Use of Cognitive Aids. BMJ Qual Saf. 2016; 25(10):733-5.
  2. Joo S, Xu T, Makary MA. Video transparency: a powerful tool for patient safety and quality improvement. BMJ Qual Saf. 2016; 25: 911-3.
  3. Lilford RJ. Can We Do Without Heroism in Health Care? NIHR CLAHRC West Midlands News Blog. March 20, 2015.

Clinical and Epidemic Outcomes from Implementation of Hospital-Based Antimicrobial Stewardship Programmes (ASPs)

The poor authors of this study had to read 24,917 citations to locate 26 studies with pre- and post-implementation comparisons.[1] The mean effect across these 26 ASPs was a 19% reduction in total antimicrobial consumption, while there was a 27% reduction in use of ‘restricted’ antibiotic agents, and an 18.5% reduction in use of broad-spectrum antibiotics. Overall hospital costs decreased by no less than 34% (mainly due to a 9% reduction in length of stay). There was a reduction in infections with resistant organisms, but no overall reduction in infection related adverse events. Of course, the interventions varied in nature and there was no attempt to classify them (say by type and intensity of intervention) and analyse the results accordingly. The study designs are generally weak, not controlling for temporal trends. The health economics is short-term and (for understandable reasons) the potential benefits of a contingent decrease in antimicrobial resistance were not modelled.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Karanika S, Paudel S, Grigoras C, Kalbasi A, Mylonakis E. Systematic Review and Meta-analysis of Clinical and Economic Outcomes from the Implementation of Hospital-Based Antimicrobial Stewardship Programs. Antimicrob Agents Chemother. 2016; 60(8): 4840-52

Unintended Consequences of Pay-For-Performance Based on Readmissions

Introducing fines for readmission rates crossing a certain threshold has been associated with reduced readmissions. Distilling a rather wordy commentary by Friebel and Steventon,[1] there are problems with the policy since it might not lead to optimal care:

  1. The link between quality of care and readmission is not good according to most studies, so that there is a risk that patients who need readmission will not get it.
  2. In support of the above, less than a third of readmissions are for the condition that caused the previous admission (which is not to say that none are preventable, but it suggests that a high proportion might not be).
  3. Risk-adjustment is at best imperfect.
  4. And this probably explains why ‘safety net’ hospitals caring for the poorest clientele come off worst under the pay-for-performance system.

I refer it my iron law of incentives – ‘only use them when providers truly believe that the target of the incentive lies within their control.’

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Friebel R, Steventon A. The multiple aims of pay-for-performance and the risk of unintended consequences. BMJ Qual Saf. 2016.

Yet More Evidence That Public Reporting of Outcomes is Without Benefit

The Centers for Medicare and Medicaid Services Hospital Compare Program added to its list of clinical process measures with mortality outcomes for three index conditions – pneumonia, heart attack, and heart failure. Across 3,970 hospitals (of sufficient size to participate) there was no improvement in mortality for the three index conditions when compared to non-index conditions. Mortality for both index and non-index conditions was declining gradually before they were added to the publically reported measures in 2008.[1] Far from an acceleration in the rate of decline post-2008, it actually slowed down. Our article on incentives suggested why public reporting is effete.[2]

— Richard Lilford, CLAHRC WM Director

References:

  1. Joynt KE, Orav J, Zheng J, Jha AK. Public Reporting of Mortality Rates for Hospitalized Medicare Patients and Trends in Mortality for Reported Conditions. Ann Intern Med. 2016; 165: 153-60.
  2. Lilford RJ. Financial Incentives for Providers of Health Care: The Baggage Handler and the Intensive Care Physician. NIHR CLAHRC West Midlands News Blog. 25 July 2014.

Another Study of Pay for Performance in Hospitals

It is such an attractive idea isn’t it. Pay more to hospitals that save more lives and penalise those that do not. Well that is exactly what the Centers for Medicare and Medicaid Services has been doing in the USA for the past few years with respect to just three medical conditions: myocardial infarction (heart attack), pneumonia, and heart failure. A three-year follow up study has now been reported comparing 2,919 participating hospitals with 1,348 control hospitals – there are a lot of hospitals in the US.[1] The main comparisons: 1) intervention vs. control hospitals; and 2) three targeted conditions vs. other conditions in the intervention hospitals. No effects were observed; intervention hospitals did no better than controls and, across interventions hospitals, the targeted conditions found no better than those that were not targeted. This finding is different to the short, but not long-term, results of a study in England,[2] [3] though this study was based on payment for compliance with process measures not outcome. The CLAHRC WM Director posits two reasons for the null result in the American study. First, mortality is insensitive to care quality.[4-6] Second, incentives work if ‘agents’ (people targeted by the incentive) think they can influence the outcome. So this is the CLAHRC WM Director’s theory – incentivise specific actions (i.e. process), not outcome, and never use hospital-wide mortality as a quality measure.

— Richard Lilford, CLAHRC WM Director

References:

  1. Figueroa JF, Tsugawa Y, Zheng T, Orav EJ, Jha AK. Association between the Value-Based Purchasing pay for performance program and patient mortality in US hospitals: observational study. BMJ. 2016; 353: i2214.
  2. Lindenauer PK, Remus D, Roman S, et al. Public reporting and pay for performance in hospital quality improvement. N Engl J Med. 2007; 356: 486-96.
  3. Kristensen SR, Meacock R, Turner AJ, et al. Long-term effect of hospital pay for performance on mortality in England. N Engl J Med. 2014; 371: 540-8.
  4. Girling AJ, Hofer TP, Wu J, Chilton PJ, et al. Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study. BMJ Qual Saf. 2012; 21(12): 1052-6.
  5. Lilford R, Mohammed MA, Spiegelhalter D, Thomson R. Use and misuse of process and outcome data in managing performance of acture medical care: avoiding institutional stigma. Lancet. 2004; 363: 1147-54.
  6. Lilford R, Pronovost P. Using hospital mortality rates to judge hospital performance: a bad idea that just won’t go away. BMJ. 2010; 340: c2016.