Measuring patient safety in New Zealand public hospitals

<p>In New Zealand the Ministry of Health recognises quality of care as an integral part of a high performing health system and identifies patient safety as one of the key dimensions of quality. Over recent years a greater emphasis has been placed on improving patient safety mostly as a result of increased awareness around the frequency of medical error and resulting economic cost. However tools used to measure patient safety are limited. In particular the use of hospital administrative data to measure patient safety is scarce and existing safety measures often ignore one of the major issues confronting comparative analyses of hospital safety, risk adjustment to control for the differences in populations hospitals serve. The objective of this research is to develop comparable measures of patient safety for New Zealand public hospitals. It uses risk adjustment strategies applied to the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs) with New Zealand hospital administrative data, the National Minimum Dataset 2001 to 2009. The research employs econometric techniques to address risk adjustment of the PSIs, utilising existing AHRQ models but adapting and re-estimating them with New Zealand administrative data. The findings from the research indicate that to use the AHRQ PSIs as measures of hospital patient safety in New Zealand, risk adjustment should first be employed to ensure measures are comparable across hospitals and over time. Overall, although the impact of risk adjustment appears to be minor, it has relevance and this should be recognised. Relative hospital performance is affected by risk adjustment. In particular, it has the greatest impact on those hospitals with poor rankings. The research takes us a step closer to being able to confidently measure patient safety and quality of care in New Zealand public hospitals in an innovative way.</p>

Measuring patient safety in New Zealand public hospitals

<p>In New Zealand the Ministry of Health recognises quality of care as an integral part of a high performing health system and identifies patient safety as one of the key dimensions of quality. Over recent years a greater emphasis has been placed on improving patient safety mostly as a result of increased awareness around the frequency of medical error and resulting economic cost. However tools used to measure patient safety are limited. In particular the use of hospital administrative data to measure patient safety is scarce and existing safety measures often ignore one of the major issues confronting comparative analyses of hospital safety, risk adjustment to control for the differences in populations hospitals serve. The objective of this research is to develop comparable measures of patient safety for New Zealand public hospitals. It uses risk adjustment strategies applied to the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs) with New Zealand hospital administrative data, the National Minimum Dataset 2001 to 2009. The research employs econometric techniques to address risk adjustment of the PSIs, utilising existing AHRQ models but adapting and re-estimating them with New Zealand administrative data. The findings from the research indicate that to use the AHRQ PSIs as measures of hospital patient safety in New Zealand, risk adjustment should first be employed to ensure measures are comparable across hospitals and over time. Overall, although the impact of risk adjustment appears to be minor, it has relevance and this should be recognised. Relative hospital performance is affected by risk adjustment. In particular, it has the greatest impact on those hospitals with poor rankings. The research takes us a step closer to being able to confidently measure patient safety and quality of care in New Zealand public hospitals in an innovative way.</p>

Describing the burden of diphtheria in Canada from 2006 to 2017, using hospital administrative data and reportable disease data

Background: Canada has maintained a low incidence of toxigenic diphtheria since the 1990s, supported by continued commitment to publicly funded vaccination programs. Objective: To determine whether hospitalization data, complemented with notifiable disease data, can describe the toxigenic respiratory and cutaneous diphtheria burden in Canada, and to assess if Canada is meeting its diphtheria vaccine–preventable disease-reduction target of zero annual cases of locally transmitted respiratory diphtheria. Methods: Diphtheria-related hospital discharge data from 2006 to 2017 were extracted from the Discharge Abstract Database (DAD), and diphtheria case counts for the same period were retrieved from the Canadian Notifiable Disease Surveillance System (CNDSS), for descriptive analyses. As data from the province of Québec are not included in the DAD, CNDSS cases from Québec were excluded. Results: A total of 233 diphtheria-related hospitalizations were recorded in the DAD. Of these, diphtheria was the most responsible diagnosis in 23. Half the patients were male (52%), and 57% were 60 years and older. Central region (Ontario) accounted for the most discharge records (61%), followed by Prairie region (Alberta, Manitoba and Saskatchewan; 23%). Cutaneous diphtheria accounted for 43% of records, and respiratory diphtheria accounted for 3%, with the remainder being other diphtheria complications or site unspecified. Two records with diphtheria as the most responsible diagnosis resulted in inpatient deaths. Eighteen cases of diphtheria were reported through CNDSS. Cases occurred in all age groups, with the largest proportions among those aged 20 to 59 years (39%) and those aged 19 years and younger (33%). Cases were only reported in the Prairie (89%) and West Coast (British Columbia; 11%) regions. Conclusion: Hospital administrative data are consistent with the low incidence of diphtheria reported in CNDSS, and a low burden of respiratory diphtheria in Canada. Although Canada appears to be on track to meet its disease-reduction target, information on endemic transmission is not available.

Understanding mortality among hematology oncology patients identified from hospital administrative data.

215 Background: Hospital administrative data has large ramifications for quality of care and quality improvement projects. Unexpected inpatient mortality among patients with a hematology/oncology (H/O) diagnostic related group (DRG) is not widely studied and reported. We sought to investigate the unexpected inpatient mortality rate among cancer patients who were admitted to a large, urban, tertiary care safety net teaching hospital. Methods: We obtained the hospital’s adjusted mortality rate and evaluated the subset of specific observed/expected deaths ratio (O/E) of patients with a primary H/O diagnosis. We reviewed each case that was identified as an unexpected inpatient mortality from 2016 to 2018. A chart abstraction tool was designed for data abstraction that included demographics, location of admission, comorbid conditions, if mortality was expected, if documentation was reflective of the severity of the illness. Two independent reviewers abstracted each chart. A third reviewer assessed each case to make a final determination regarding expected mortality based on medical complexity and if documentation was reflective of severity of illness. Results: On review of the inpatient mortality data, the O/E for the H/O diagnostic related group (DRG) from Vizient was 1.14. Twenty two cases were identified as having an unexpected death. Among those cases, 23% of patients were transferred from an outside hospital, and 23% of patients did not have a known cancer diagnosis on admission. In the majority of cases, initial documentation did not accurately reflect severity of illness and/or specialists in hematology or oncology were not consulted at time of diagnosis. We noted the majority of patients were not followed by a hematologist/oncologist within the hospital system (63.6%). In 55% of patients, the cause of death was acute respiratory failure, and 14% of patients had an Advance Directive/DNAR in the chart prior to admission. Delayed antibiotics in febrile neutropenia and a missed blood transfusion reaction were among causes of unexpected deaths. Conclusions: Hospital generated data reported that mortality among H/O patients was greater than projected, however careful chart review of each case demonstrated a significant number of cases that were expected but the severity of illness was not documented properly to account for the death. Templates for proper documentation using the Vizient mortality variables is a key area to lower unexpected inpatient mortality.

Examining the Patterns of Virtual Visit Uptake Across Clinical Disciplines Using Hospital Administrative Data During the COVID-19 Pandemic: A Descriptive Study. (Preprint)

BACKGROUND The COVID-19 pandemic resulted in a dramatic and rapid shift away from physical visits, resulting in an instantaneous and unplanned adoption of virtual (phone and video) visits. OBJECTIVE Administrative data at a large urban Canadian university-affiliated hospital network was analyzed to understand how clinical disciplines adopted phone and video visits early in the pandemic and how their use of virtual visits grew, sustained or was abandoned through the pandemic. METHODS Virtual visit adoption by clinical discipline was compared during both the early pandemic (Apr-May 2020) and peak reopening time periods (Oct-Nov 2020) in an attempt to categorize clinical disciplines by their adoption of virtual visits, and thus understand how best to provide change management support. RESULTS At our largest academic site, for which we had full data, 50.8% of ambulatory visits were provided by phone or video during the pandemic (94.5% phone, 5.5% video). There was considerable variability across services in terms of how they adopted virtual visits in the early pandemic and peak reopening. Phone was the dominant modality, but video had high usage (up to 95% of virtual visits) in select disciplines. CONCLUSIONS We identified 4 patterns that provide opportunities for dedicated support in some disciplines: early sustained adoption, non-adoption, late growth, and late abandonment. The phone was the dominant modality (>90% of virtual visits) but video had high use in some disciplines necessitating targeted support. Additional high-quality research examining phone vs. video visits across disciplines and contexts is critical; until then both should be supported.

Verification of administrative data to measure palliative care at terminal hospital stays

Background: Administrative data and clinician documentation have not been directly compared for reporting palliative care, despite concerns about under-reporting. Objective: The aim of this study was to verify the use of routinely collected administrative data for reporting in-hospital palliation and to examine factors associated with coded palliative care in hospital administrative data. Method: Hospital administrative data and inpatient palliative care activity documented in medical records were compared for patients dying in hospital between 1 July 2017 and 31 December 2017. Coding of palliative care in administrative data is based on hospital care type coded as “palliative care” and/or assignment of the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) palliative care diagnosis code Z51.5. Medical records were searched for specified keywords, which, read in context, indicated a palliative approach to care. The list of keywords (palliative, end of life, comfort care, cease observations, crisis medications, comfort medications, syringe driver, pain or symptom management, no cardiopulmonary resuscitation, advance medical plan/resuscitation plan, deteriorating, agitation, restless and delirium) was developed in consultation with seven local clinicians specialising in palliative care or geriatric medicine. Results: Of the 576 patients who died in hospital, 246 were coded as having received palliative care, either solely by the ICD-10-AM diagnosis code Z51.5 (42%) or in combination with a “palliative care” care type (58%). Just over one-third of dying patients had a palliative care specialist involved in their hospital care. Involvement of a palliative care specialist and a cancer diagnosis substantially increased the odds of a Z51.5 code (odds ratio = 11 and 4, respectively). The majority of patients with a “syringe driver” or identified as being at the “end of life” were assigned a Z51.5 code (73.5% and 70.5%, respectively), compared to 53.8% and 54.7%, respectively, for “palliative” or “comfort care.” For each keyword indicating a palliative approach to care, the Z51.5 code was more likely to be assigned if the patient had specialist palliative care input or if they had cancer. Conclusion: Our results suggest administrative data under-represented in-hospital palliative care, at least partly due to medical record documentation that failed to meet ICD-10-AM coding criteria. Collaboration between clinicians and coders can enhance the quality of records and, consequently, administrative data.