Counties Manukau District Health Board System Level Measures

Recommendation It is recommended that the Executive Leadership Team review and discuss the following: 1. SLMs Dashboard 2. Analysis and ‘drill down’ of SLMs: -Emergency Department Length of Stay – 6 Hour Pass Percentage -Un-enrolled Health Service Utilisation -Hospital Standardised Mortality Ratio Prepared and submitted by Naina Raj, Mataroria Lyndon, Sybil Hau Approved by: Clinical Governance Group Key findings:

1) SLMs Dashboard

• Positive improvement for the the SLMs; Life Expectancy at Birth, Childhood Immunisation Status, Long Term Conditions Risk Assessment and Risk Management, and Access to Diagnostics.

• Common cause variation1 for the SLMs; ASH, Hospital Days During the Last 6 Months of Life, Rate of Adverse Events, ED Length of Stay, HSMR, Access to Elective Surgery, and Acute Hospital Readmission.

• For the Health Service Utilisation SLM, due to current lack of sufficient data points we are unable to display any trends. Further data is pending for Healthcare Cost Per Capita, and Patient Experience of Care SLMs.

2) Emergency Department Length of Stay – 6 Hour Pass Percentage

• Since 2009/2010, for 18 consecutive quarters, CMH has been meeting the Health

Target; however, further improvement is required to raise our Quarter Three 2013/2014 ranking from ninth place.

1 In statistical terms ‘common causes’ are also ‘natural patterns’ that are the usual regular and predictable variations in the system while ‘special causes’ are new, unanticipated outside the usual variations in the system that warrants for further investigation.

• The most common reasons for not meeting the 6 hour health target is longer waiting time from triage to be seen by a decision making clinician, and availability of inpatient beds.

3) Un-enrolled Health Service Utilisation

• 3.6% of inpatient discharges were not enrolled in a PHO within a month from the day of discharge in both 2012, and 2013.

• The un-enrolment rate post discharge is similar to other publicly funded hospitals in the Auckland metro region.

• Infants and young children aged 0 to 4 years along with the 20 to 29 year olds have among the highest rates of un-enrolment within a month of discharge in 2013.

• Higher proportions of Maaori, Pacific, and Asian people are un-enrolled one month post discharge compared to NZ European/Other groups.

4) Hospital Standardised Mortality Ratio

• The HSMR for CMH is 83, which is below the Health Roundtable average of 100, and is approaching the IHI benchmark of 75.

• The HSMR for CMH is lower during weekdays, when compared to weekends. • There is no major variation in the HSMR between males and females. It is difficult to

draw any firm conclusions on age and ethnic differences in the HSMR. This is mainly due to the lack of enough data points (ie low number of cases) that is required to detect any significant variation.

Aim 3 - Cost & ProductivityAim 1 - Population Health Aim 2 - Patient Experience(D

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Counties Manukau District Health Board System Level Measures

Emergency Department Length of Stay Six Hour Health Target Pass Percentage

Recommendation It is recommended that the Executive Leadership Team review and discuss the following:

1. Analysis and ‘drill-down’ of the Emergency Department (ED) Length of Stay (LOS) SLM

2. Potential contributory factors associated with ED LOS for CMH

3. Identify potential areas for improvement/intervention

Prepared and submitted by: Naina Raj, Mark Ng, Cherian Thomas, Mataroria Lyndon Approved by: Alex Boersma, Vanesa Thornton 1. Purpose System Level Measures drill down: Emergency Department Length of Stay (Six Hour Health Target Pass %) 2. Background Taking a whole of system view, a well-functioning Emergency Department (ED) is an important component of any healthcare system. However, a significant issue for many ED departments is overcrowding, which is associated with prolonged patient length of stay.1 In considering its potential consequences such as delays to patient care, and higher risk of adverse events, it requires thorough investigation. This report is the fourth in a series of drill-downs to be presented as part of the SLMs. The purpose of this report is to present an overview of the ED length of stay in CMH in order to understand the quality of care provided, and the performance of our healthcare system. This report presents an overview of the analysis completed for this SLM, and a drill-down of its contributory factors.

1 Ministry of Health. (2009). The impact of emergency department (ED) overcrowding on in-patient length of stay, mortality and time critical conditions. Ministry of Health: Wellington

Key Findings

• Since 2009/2010; for 18 consecutive quarters, CMH has been meeting the Health Target; however, further improvement is required to raise our Quarter Three 2013/2014 ranking from ninth place.

• The most common reasons for not meeting the 6 hour health target is longer waiting time from triage to be seen by a decision making clinician, and availability of inpatient beds.

3. Proposal According to literature patient length of stay (LOS) in ED can act as valuable performance measure that has the potential for enabling improvement in other areas of the hospital. In

May 2009, the Minister of Health introduced six national health targets for New Zealand. The first target was ‘Shorter stays in emergency department’ defined as 95% of patients will be admitted, discharged or transferred from an ED within 6 hours. However, the

measure on its own does not ensure quality of care. Since many causes of prolonged ED LOS falls outside of the ED department alone, a whole of system approach needs to be taken across the continuum of care to improve ED waiting times, and overall health outcomes.2 The rationale for reporting on the SLMs is to provide robust information to support progress toward and achievement of the CMH ‘Triple Aim’ of improving population health, patient experience, and cost and productivity. If our system is performing at the highest level, then this will be reflected in those contributory measures which flow into the SLMs. Furthermore, if we could be performing better on an SLM, it is an indication that we should identify how and where processes need to be improved within our system. With this point in mind, it is pertinent that a drill-down into ED LOS is completed, with a focus on quality improvement. Improvement activities currently in place In 2008/09 the successful 6 hours can be hours campaign was launched and as part

of this campaign there was a ‘whole of hospital’ approach with regular weekly meetings identifying the issues using A3 methodology.

Since then we have focused on sustaining the 6 hour target, despite the continuing increase in patients presenting to the emergency department and the increase in self referrals as a percentage of presenting patients.

We have introduced clinical nurse specialists with a comprehensive regional training

programme.

2 Jones et al. Implementing performance improvement in New Zealand emergency departments: the six hour time policy national research project protocol. Health services Research 2012, 12:45

We have improved the MOSS cover overnight albeit only 4 days a week.

We are working towards Paediatric Credentials for the training of emergency

paediatric fellows. A suite of clinical quality indicators is being worked on and the detail of these is in

the body of this paper and we are working on the ED mandatory Quality Framework. Nursing / Medical simulation training programme.

Working with Localities to understand acute demand.

1. How are we performing? System Level Measure

1.1. Emergency Department Length of Stay

Notwithstanding, a slight reduction in performance on the 6 hour pass percentage rate in early 2014, on average 96% of patients presenting to Emergency Care in CMH are admitted, transferred or discharged within 6 hours of arrival. This reduction in performance was associated with increased volumes for this time of year, and restricted availability of inpatient beds due to AT&R refurbishment for a 6 week period.

How are we performing against other similar hospitals?

Since 2009/2010, for 18 consecutive quarters, CMH has been meeting the Health Target; however, further improvement is required to raise our Quarter Three 2013/2014 ranking from ninth place

http://www.health.govt.nz/new-zealand-health-system/health-targets/how-my-dhb-performing/how-my-

Quarter Two 2013/2014 Quarter Three 2013/2014

2. Contributory Measures The following section includes a discussion of the contributory factors for this SLM, comparative analysis where possible, and recommendations for improvements.

4.1 Reasons for Delays in ED

Primary and secondary causes of delays >6hrsdata for 2013 (Excl AOU). Overall breaches = 3.4% (Clinical = 0.5%)

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The above graph shows the primary and secondary reasons for delay in patients passing through ED within 6 hours for 2013. The most common reason for not meeting the 6 hour health target is longer waiting times from triage to be seen by a decision making clinician, followed by availability of inpatient beds. The third most common reason for the delay was due to patients who were clinically indicated to remain in ED beyond 6 hours (ie requiring further ED input).

4.2 To Be Seen Time-Triage Category Two (TC1-2)

4.3 To Be Seen Time-Triage Category Three (TC1-3)

CMH uses the Australian Triage Scale to ensure that patients are seen in order of their clinical urgency. The scale has five categories; Triage Category 1 are immediate life threatening ones in contrast to Triage Category 5 that are less urgent, or dealing with administrative issues only. The target for Triage Category Two time is 10 minutes and Category Three 30 minutes. Currently about 60-70% of TC1-2 patients are seen within the target at a median seen time of 22 minutes. Only 20 to 25% of TC1-3 patients are seen within the target at a median seen time of 104 minutes. However, as seen in the above two charts there has not been any significant variations in the percentages of patients seen for both Triage two and Triage three over the years. N.B we are currently exploring to replace these with control charts since any statistical variation is more visible in this.

How are we performing against other similar hospitals? As seen in the below HRT charts for the period of July-Dec 2013, CMH performed in the lowest quartile in terms of overall triage performance and Triage Category Two and Three.

Triage Two HRT Performance

Triage Three HRT Performance

4.4 LOS in inappropriate places-Monitor Corridor (MCCOR) Volume

The above charts shows around 0.5 to 10% of patients are placed in the monitor corridor areas with an average length of stay between 10-15 minutes.

4.5 Time to antibiotics, analgesia, percutaneous Intervention (PCI).

The following contributory measures are antibiotic, analgesia, and PCI timing for acute conditions. Time to Percutaneous Intervention

% patients getting PCI within 90 minutes

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Percutaneous intervention within fours for patients with acute myocardial infarction, is also a standard quality measure for ED departments. There has been good progress in the percentage of patients getting PCI within 90 minutes over the last 3 years. Although some variation is seen in the above charts there has been a steep rise in the patients getting timely PCI from October 2013 due to quality improvement initiatives.

Time to analgesia in renal colic

The percentage of patients presenting with Renal Colic and receiving analgesia within 30 minutes of arrival has been well below target, however, there are quality improvement initiatives currently underway to improve performance. Time to antibiotics in sepsis

Average time to antibiotics for all patients presenting to EC with sepsis / severe sepsis

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Antibiotic administration within two hours of arrival of patients with sepsis is a standard quality measure for ED departments. Research shows an inverse relationship between the timing of patients receiving antibiotics and ED overcrowding. For example, delay in antibiotic administration causes prolonged length of patient stay leading to ED

% Patients presenting with Renal Colic receiving analgesia within 30 minutes of arrival

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% Renal Colic patients given analgesia within 30 minutes of arrival Target

overcrowding. This places significant pressure on ED resources compromising the clinician’s capacity to deliver care within required time frames and quality standards. The above chart shows a pattern of common cause variation in the time to antibiotics for patients presenting with EC.3 The overall administration time is within the 4 hour recommended standard. 5 Future Steps This document provides a high level analysis of ED LOS System Level Measure. Feedback is required from the Executive Leadership Team to ensure that the approach to reporting this SLM, the associated contributory measures, and the recommendations are relevant and informative. It is recognised particularly that:

• Further analyses of the contributory factors are required. • Information pertaining to demographic and clinical characteristics (where available)

of patients presenting to CMH ED. • Revision of the charts to control charts • Further improvement activities are required to improve performance on this SLM.

3 In statistical terms ‘common causes’ are also ‘natural patterns’ that are the usual regular and predictable variations in the system while ‘special causes’ are new, unanticipated outside the usual variations in the system.

Counties Manukau District Health Board System Level Measures

Un-enrolled Health Service Utilisation Recommendation It is recommended that the Executive Leadership Team review and discuss the following:

1. Un-enrolled Health Service Utilisation SLM

2. Potential contributory factors associated with this SLM

3. Identify potential areas for improvement/intervention

4. The limitations of the traditional methods in measuring PHO enrolment, particularly in relation to ethnicity

Prepared and submitted by: Wing Cheuk Chan, Dean Papa, Doone Winnard, Naina Raj, Mataroria Lyndon Approved by: Clinical Governance Group 1. Purpose System Level Measures drill down: Un-enrolled Health Service Utilisation 2. Background The advantages of PHO enrolment include lower co-payment for primary care visits and lower prescription charges when care is provided by the nominated general practice team. Furthermore, people enrolled in a PHO have the benefit of a primary healthcare team to co-ordinate a wider range of health services including opportunistic and/or proactive preventive care. At a high level PHO enrolment for the CM Health population has a coverage of 97% in 2014 with the lowest PHO enrolment rates seen in 15 to 29 year males (see appendix). However, indicators of enrolment by ethnicity have suggested that, for instance, Maaori enrolment is lower than this at 89-90% for CM Health and concern has been expressed regionally about low PHO enrolment for Asian populations. Ideally, all eligible New Zealand residents should be enrolled. However, PHO enrolment is voluntary and people who are not enrolled may be well, and not have any immediate health needs. Therefore, this system level measure is designed to highlight the subgroups of

people who are not enrolled and are likely to benefit most from PHO enrolment (i.e. the groups that have significant health needs). Recent health service utilisation, in particular recent hospitalisation can be a useful proxy of recent health need. Therefore, people who were not enrolled within a month after being discharged from Middlemore Hospital has been suggested as the main system level measure of “Un-enrolled Health Service Utilisation”. Timely enrolment can be a helpful step to ensure patients receive the appropriate follow up. 3. Strategic Fit

This SLM aligns with the Better Health Outcomes for All executable strategy. Improvement of this SLM may lead to improvement in “Improved quality, safety and experience of care” and “Improved health and equity for all Populations” dimensions of the CM Health Triple Aim by reducing the cost barriers in accessing primary healthcare for those people previously

unenrolled, and by enabling access to a wider range of co-ordinated primary healthcare services. 4. Methodology

SLM: The percentage of people discharged from Middlemore Hospital in 2013 who were unenrolled within one month of discharge. Definitions: Denominator: Casemix acute, arranged and elective discharges from Middemore Hospital in 2013.1 People who died within one month of discharge were excluded. Numerator: The number of people discharged from Middlemore who are not enrolled (anywhere in NZ) in 2013 within a month of discharge.2 Limitations There are number of late entries in the PHO enrolment. People who are deceased may remain in PHO enrolment dataset for a number of quarters. 5. How are we performing?

1 One individual may be discharged more the once in a year. 2 4 quarters of PHO enrolment data in 2014 and first 2 quarters of PHO enrolment data were examined to ensure some of enrolment that occurred in 2013 but entered late in PHO enrolment were captured. The start date of enrolment as record in the PHO enrolment record was used. The latest/ end date of enrolment to determine by the latest quarter an individual is present in the PHO quarter. The cut off dates for financial claims were used, e.g if an individual is last present in Q1 then assume person is enrolled on 20th Nov in the previous year, Q2: 20th Feb in the year of interest, Q3: 20th May, Q4:. 20th August,

5.1 “Un-enrolled Health Service Utilisation” (Middlemore Hospital)

Table 1: Percentage of PHO un-enrolment one month post discharge from Middlemore Hospital in 2012 and 2013

Middlemore Hospital Manukau Surgical Centre/ Super Clinic

Year 2012 2013 2012 2013 Number of case mix discharges

65,824 66,239 10,475 10,839

Number of people discharged not enrolled within a month of discharge

2,650 2,627 120 145

Percentage of un-enrolment one month post hospital discharge

4% 4% 1% 1%

Overall, about 3.6% of inpatient discharges from MMH and Manukau Surgical Centre combined were not enrolled in a PHO within a month from the day of discharge in both 2012, and 2013. The percentage was lower for those discharged from the Manukau Surgical Centre than from MMH. This would seem logical given that discharges from MSC are from elective procedures and referral from primary care is usually part of the journey to get to the event of surgery. Infants and young children aged 0 to 4 years along with the 20 to 29 year olds have among the highest rates of un-enrolment within a month of discharge in 2013. Since there is now an automatic system of nominating primary care provider for all newborns, the enrolment rate of infants is likely to improve over time once the issues of getting declined nominations have been worked through (Personal communication, Dr Pip Anderson). Table 2: Number and percentage of PHO un-enrolment post discharge from Middlemore Hospital in 2013 (comparison between one month post discharge vs a simplified method using enrolment as at Q2 2014 PHO enrolment)

Age Number of un-enrolment

within a month of discharge

Percentage of un-enrolment

within a month of discharge

Percentage of un-enrolment as at Q2

2014 PHO enrolment data (simplified

‘annual’ method)

Number of case mix discharges

from MMH

00-04 477 6.5% 5.1% 7,295 05-09 76 3.7% 4.1% 2,066 10-14 68 3.7% 3.7% 1,817 15-19 187 5.4% 5.6% 3,455 20-24 303 7.2% 7.1% 4,210 25-29 288 8.0% 7.2% 3,619 30-34 189 5.4% 5.0% 3,469 35-39 154 4.7% 4.9% 3,293

40-44 148 3.8% 3.7% 3,861 45-49 132 3.2% 3.6% 4,124 50-54 135 3.0% 3.3% 4,461 55-59 110 2.7% 3.3% 4,089 60-64 95 2.3% 3.1% 4,128 65-69 56 1.4% 2.2% 3,945 70-74 63 1.7% 2.8% 3,691 75-79 62 1.9% 2.9% 3,251 80-84 45 1.6% 3.3% 2,823 85+ 39 1.5% 3.9% 2,642

Total 2,627 4.0% 4.2% 66,239 Table 3 and Table 4 lists the number of un-enrolment by age group and ethnicity. Higher proportions of Maaori, Pacific, and Asian people are un-enrolled one month post discharge compared to NZ European/Other groups. Table 3: Number of PHO un-enrolment one month post discharge from Middlemore Hospital in 2013 by ethnicity

Age Maaori Pacific Asian NZ European and others Total

00-04 130 214 76 57 477 05-09 15 35 13 13 76 10-14 11 36 13 8 68 15-19 32 78 40 37 187 20-24 56 103 73 71 303 25-29 63 76 81 68 288 30-34 31 68 34 56 189 35-39 33 51 25 45 154 40-44 27 47 20 54 148 45-49 29 47 19 37 132 50-54 17 52 19 47 135 55-59 19 43 18 30 110 60-64 13 33 17 32 95 65-69 9 26 7 14 56 70-74 6 31 6 20 63 75-79 5 20 17 20 62 80-84 <5 14 12 17 45 85+ <5 <5 <5 32 39

Total 501 977 491 658 2,627

Table 4: Percentage of PHO un-enrolment one month post discharge from Middlemore Hospital in 2013 by ethnicity

Age Maaori Pacific Asian NZ European and others Total

00-04 7% 6% 10% 5% 7% 05-09 3% 4% 5% 3% 4% 10-14 2% 5% 8% 2% 4% 15-19 4% 7% 14% 3% 5% 20-24 5% 7% 17% 6% 7% 25-29 7% 7% 15% 6% 8% 30-34 4% 7% 6% 5% 5% 35-39 5% 5% 5% 4% 5% 40-44 4% 4% 4% 3% 4% 45-49 3% 4% 4% 2% 3% 50-54 2% 5% 4% 2% 3% 55-59 3% 4% 4% 2% 3% 60-64 2% 3% 3% 2% 2% 65-69 2% 3% 2% 1% 1% 70-74 1% 4% 1% 1% 2% 75-79 2% 3% 6% 1% 2% 80-84 1% 4% 5% 1% 2% 85+ 6% 2% 1% 1% 1%

Total 4% 5% 7% 2% 4% How are we performing against other similar hospitals? At a high level, the un-enrolment rate post discharge is similar to other publicly funded hospitals in the Auckland metro region. International comparisons are not available as this is a new indicator created by CM Health population health team to try to capture and demonstrate the opportunities for improvement for an important element of whole of system care. Only countries that have a unique identifier for all health service users could undertake this analysis robustly. Table 5: Percentage of hospital casemix discharges in 2013 that were not enrolled as per 2014 Q2 PHO enrolment dataset (simplified method)

Hospital Percentage of un-enrolment post discharge Middlemore Hospital +Manukau Surgical Centre

3.9%

North Shore Hospital 4.2%

Waitakere Hospital 3.7% Auckland City Hospital 4.0%

6. Contributory Measures The following section identifies potential contributory measures for this SLM. (a) Un-enrolment rate for people who have had more than one admission The un-enrolment rate was lower for people who had multiple admissions to Middlemore Hospital in 2013 – 2.7% for people who had more than one admission, 4.5% for those who had only one admission. This is encouraging, that ‘somewhere in the journey’ those who are admitted more than once are receiving support, or seeking for themselves, to be enrolled. The challenge is to make that support systematic. Table 6: Percentage of PHO un-enrolment one month post discharge from Middlemore 2013 by first admission and readmissions

Admission type Maaori Pacific Asian

NZ European

and others

Overall

First admission in 2013

Number 8,760 13,405 5,614 19,368 47,147

% of un-enrolment 4.6% 5.7% 7.6% 2.7% 4.5%

More than 1 hospital

admission of any cause in

2013

Number 3,818 5,302 1,845 8,127 19,092

% of un-enrolment 2.6% 4.1% 3.5% 1.6% 2.7%

Total Number 12,578 18,707 7,459 27,495 66,239

% of un-enrolment 4.0% 5.2% 6.6% 2.4% 4.0%

(b) Un-enrolment rate by speciality team of discharge The initial opportunity for system improvement may be at the ‘front of hospital’ since people discharged from the Emergency Medicine team constituted the largest number of those not enrolled in a PHO at one month from discharge. As most patients are admitted via emergency department or assessment and observation units, having a system in the front of hospital is likely to benefit patients from all sub-specialities. Table 7: The number of PHO un-enrolment one month post discharge from Middlemore 2013 by speciality type

Speciality Number of hospital discharges not enrolled in a PHO within a month

M05 Emergency Medicine 674 S60 Plastic Surgery [excluding burns] 408 M00 General Medicine 374 M55 Paediatric Medicine 353

S00 General Surgery 253 S45 Orthopaedic Surgery 215 S30 Gynaecology 125 S20 Dental Surgery 57 M10 Cardiology 40 M60 Renal Medicine 36 M65 Respiratory Medicine 24 M30 Haematology 20 Other 48 Grand Total 2,627

(c) Un-enrolment by primary diagnosis Significant proportions of the admissions may be preventable. Conditions such as asthma, urinary tract infections, gastroenteritis and cellulitis may be preventable if patients sought care earlier in primary care and this could be facilitated by enrolment. (Some of the cases related to trauma might not be preventable by PHO enrolment). Table 8: Number of hospital discharges from MMH not enrolled in a PHO within a month by primary diagnosis

Primary Diagnosis Number of Hospital discharges not enrolled in a PHO within a month

A099 Gastroenteritis and colitis of unspecified origin 54 J459 Asthma, unspecified 50 N390 Urinary tract infection, site not specified 46 B349 Viral infection, unspecified 40 J218 Acute bronchiolitis due to other specified organisms 39

J069 Acute upper respiratory infection, unspecified 39 J189 Pneumonia, unspecified 36 R074 Chest pain, unspecified 32 S6263 Fracture of distal phalanx 30 S610 Open wound of finger(s) without damage to nail 28

R062 Wheezing 27 R104 Other and unspecified abdominal pain 26 J219 Acute bronchiolitis, unspecified 26 L0311 Cellulitis of lower limb 24 R073 Other chest pain 24 L024 Cutaneous abscess, furuncle and carbuncle of limb 23

R55 Syncope and collapse 23 J210 Acute bronchiolitis due to respiratory syncytial virus 23

J22 Unspecified acute lower respiratory infection 22 S663 Injury of extensor muscle and tendon of other finger at wrist and hand level 21

I500 Congestive heart failure 21 S0265 Fracture of angle of jaw 19

R101 Pain localised to upper abdomen 19 R103 Pain localised to other parts of lower abdomen 17 O034 Spontaneous abortion, incomplete, without complication 16

K590 Constipation 16 O021 Missed abortion 16 J440 Chronic obstructive pulmonary disease with acute lower respiratory infection 16

7. Next Steps Discuss the value of this SLM and the possible actions that may be required for further improvement. Identify the member(s) from the ELT team who will be responsible to facilitate improvement of this indicator. Consider adding to the data integration agenda as part of Project Swift the potential to have a National PHO enrolment lookup to enable a nominated GP to be identified and validated. Liaise with the Population Health Team regarding technical questions and challenges in relation to data linkage.

Draft recommendations for improvement: • Discharge planning needs to start at admission. • Exploration of the current process of verifying a patient’s GP and making this more

robust as part of admission processes. • Processes to verify a patient is actually enrolled in a PHO through the nominated GP

practice (explore the possibility of the PHO enrolment lookup like the NHI) • Build on existing process of nominating a primary care provider for the new born to

assist people of other age groups who aren't enrolled to find a practice and facilitate their enrolment.

• Discuss whether active un-enrolment by PHOs or practices, as noted in the after hours report, should be explored further.

Appendix One Table 1 Comparison between Counties Manukau DHB Population Projections from Statistics New Zealand in 2014, and PHO enrolment 2014 Q2 by age groups

Age groups Population Projections from Stats NZ in 2014

Number of people enrolled as per 2014

Q2 register PHO enrolment

Estimated percentage of PHO

enrolment3

00-04 41,790 41,320 99% 05-09 41,630 42,511 102% 10-14 40,220 39,163 97% 15-19 39,800 37,414 94% 20-24 41,040 37,626 92% 25-29 37,890 34,155 90% 30-34 34,120 33,052 97% 35-39 31,710 31,649 100% 40-44 35,280 35,070 99% 45-49 35,640 34,919 98% 50-54 33,950 32,897 97% 55-59 28,480 27,304 96% 60-64 23,620 23,092 98% 65-69 20,200 19,318 96% 70-74 14,510 13,788 95% 75-79 9,650 9,406 97% 80-84 6,300 6,299 100% 85+ 5,200 5,489 106% Overall 521,030 504,472 97% Females of child bearing age ‘appear’ to have a relatively high level of enrolment. Males between the ages of 15 to 29 ‘appear’ to have a lower level of enrolment. Maaori PHO enrolment is much lower that might be expected when compared to population projections from Statistics New Zealand (“90% enrolment” in 2014) (Table 2). On the other hand, Pacific PHO enrolment is much higher than the number expected based on the corresponding population projections (“108% enrolment” in 2014). This finding has been long standing and it is a result of an artefact created by “numerator denominator mismatch” between PHO enrolment and population projections (Stats NZ). Individuals may report different ethnicities in different locations or context (Census vs electoral roll vs health sector) and the quality of capturing the ethnicity data can be variable in different locations and settings. The way the health sector prioritises ethnicities within the health data may create an inadvertent artefact of undercounting Maaori if multiple ethnicities of individuals are not fully captured as in the case of PHO enrolment. The way capitation funding is determined may also have a role in how the ethnicity data is recorded.4

3 Estimated percentage of PHO enrolment = number of people enrolled divided by estimated population from Stats NZ in CMDHB in the corresponding age group in 2014. 4 http://www.health.govt.nz/our-work/primary-health-care/primary-health-care-services-and-projects/capitation-rates

Enrolling Maaori or Pacific people attracts a higher level of capitation payment than other ethnicities. However, there is no additional financial gain in accurately differentiating between Maaori and Pacific ethnicities (or recording multiple ethnicities). Statistics New Zealand has acknowledged that there has been a flattening trend in the number of births in recent years. However, the recent fall in Maaori and Pacific births have not factored into the latest population projections (version 2013). (The Population health team is currently exploring the association between the increase in the use of long term contraceptive use and the recent fall in the number of births). This is also likely to impact the difference between population projections and PHO enrolment until it is clear that the current trends of flattening are going to continue and projections are recalibrated. Table 2: Estimated percentage of PHO enrolment in Counties Manukau DHB in 2014, by gender and ethnicity

Age groups All Female All Male Maaori/Pacific Non Maaori/Pacific 00-04 98% 100% 93% 107% 05-09 102% 103% 97% 109% 10-14 98% 97% 98% 96% 15-19 95% 93% 97% 91% 20-24 97% 86% 103% 82% 25-29 97% 84% 103% 82% 30-34 99% 94% 106% 91% 35-39 100% 99% 104% 97% 40-44 99% 100% 105% 96% 45-49 97% 99% 104% 95% 50-54 97% 97% 106% 93% 55-59 96% 95% 102% 94% 60-64 97% 98% 103% 96% 65-69 95% 97% 100% 94% 70-74 95% 96% 102% 93% 75-79 96% 99% 102% 96% 80-84 102% 97% 96% 101% 85+ 105% 106% 107% 105% Overall 98% 96% 100% 94%

The proposed Un-enrolled Health Service Utilisation indicator is to apply a method that addresses many of the issues related to the numerator denominator bias noted above.5 By using encrypted NHI linkage of health datasets (rather than using the estimated resident population as the denominator), the ethnicity record in both numerator and denominator are identical for an individual.

5 The method that addresses “numerator and denominator bias” has been published in international peer review medical journal previously. http://bmjopen.bmj.com/content/4/4/e003975.full.pdf

Percentage of people unenrolled within the CM Health Service Utilisation population Definitions: • Denominator: Constructed population/ Health Service Utilisation population 2013:

People who were domiciled in Counties Manuaku in 2013 and enrolled in a PHO or had a publicly funded health service contact in 2013, namely inpatient and outpatient services, pharmaceutical dispensing, Community laboratory test, GMS claims. Deaths up to 31st Dec 2013 were excluded.

• Numerator: within the constructed population/ health service utilisation population, the percentage of people who were enrolled in a PHO (anywhere in NZ) at time some point during 2013, was determined by record linkage at encrypted NHI level. Six quarters of PHO enrolment (2013 Q1-4, 2014 Q1-2) were used to determine the enrolment status in 2013, because some of the 2013 enrolment statuses were subsequently recorded late in the 2014 PHO enrolment dataset.

Data sources: i. Ministry of Health:

a) National Minimum Dataset (hospital events; NMDS) b) National Non-admitted Patient Collection (outpatients, ED and community visits;

NNPAC) c) Pharmaceutical Collection (PHARMHOUSE) d) Laboratory Claims Collection e) Primary Health Organisation (PHO) Enrolment Collection f) General Medical Subsidy Data Mart g) National Mortality Collection

Table 3: Percentage of PHO enrolment within the CM health service utilisation population in 2013

Ethnicity

Enrolled Not enrolled

Number of people in the CM

health service utilisation population

Percentage of enrolment

Maaori 85,436 1,457 86,893 98.4% Pacific 130,985 3,150 134,135 97.7% Asian 97,302 2,357 99,659 97.7% New Zealand European and Others

198,228 3,472 201,700 98.3%

Overall 511,951 10,436 522,387 98.0% In CM Health, out of the people who had contact with publicly funded health services in 2013, 98% were enrolled. In other words, only 2% of CM population who had used publicly funded health service were not enrolled. The percentage of enrolment is similar across the selected ethnicities. Overall, females have a marginally higher PHO enrolment than males.

Table 4: Percentage of PHO enrolment within the CM health service utilisation (HSU) population by age and gender

Age

Females Males

Enrolled Not enrolled

Number of people in the CM

HSU pop’n

% of enrolm’t Enrolled Not

enrolled

Number of people in the CM

HSU pop’n

Percentage of

enrolm’t

00-04 20,979 719 21,698 96.7% 22,361 802 23,163 96.5% 05-09 21,077 304 21,381 98.6% 22,153 342 22,495 98.5% 10-14 19,357 288 19,645 98.5% 20,422 323 20,745 98.4% 15-19 19,158 394 19,552 98.0% 19,219 432 19,651 97.8% 20-24 20,012 516 20,528 97.5% 18,668 709 19,377 96.3% 25-29 18,591 440 19,031 97.7% 16,057 585 16,642 96.5% 30-34 18,034 323 18,357 98.2% 15,029 486 15,515 96.9% 35-39 17,278 249 17,527 98.6% 14,748 414 15,162 97.3% 40-44 18,945 243 19,188 98.7% 16,669 436 17,105 97.5% 45-49 18,298 224 18,522 98.8% 17,000 341 17,341 98.0% 50-54 17,044 229 17,273 98.7% 16,049 305 16,354 98.1% 55-59 14,055 171 14,226 98.8% 13,421 224 13,645 98.4% 60-64 11,884 161 12,045 98.7% 11,351 155 11,506 98.7% 65-69 9,821 97 9,918 99.0% 9,494 109 9,603 98.9% 70-74 7,152 91 7,243 98.7% 6,645 78 6,723 98.8% 75-79 5,038 67 5,105 98.7% 4,338 60 4,398 98.6% 80-84 3,618 48 3,666 98.7% 2,670 18 2,688 99.3% 85+ 3,398 31 3,429 99.1% 1,918 22 1,940 98.9% Overall 263,739 4,595 268,334 98.3% 248,212 5,841 254,053 97.7%

Table 5: Percentage of PHO enrolment within the CM health service utilisation population by age and ethnicity

Age Maaori Pacific Asian NZ European and others

00-04 97.2% 96.5% 95.6% 97.0% 05-09 99.0% 98.1% 98.1% 99.0% 10-14 98.7% 97.8% 98.6% 99.1% 15-19 98.1% 97.8% 97.5% 98.1% 20-24 97.7% 97.1% 94.7% 97.5% 25-29 98.1% 97.6% 96.1% 96.9% 30-34 98.3% 97.8% 97.7% 97.0% 35-39 98.2% 98.0% 98.4% 97.6% 40-44 98.2% 98.0% 98.7% 97.9% 45-49 98.5% 98.3% 98.5% 98.4% 50-54 98.7% 98.1% 98.6% 98.4% 55-59 99.0% 98.2% 98.6% 98.6% 60-64 99.3% 97.9% 98.5% 98.8% 65-69 99.4% 97.9% 98.9% 99.2%

70-74 99.4% 97.3% 98.6% 99.1% 75-79 99.7% 96.3% 97.6% 99.3% 80-84 99.3% 96.9% 97.7% 99.4% 85+ 98.1% 96.9% 97.7% 99.3% Overall 98.3% 97.7% 97.6% 98.3% In fact, Pacific people appear to have a slightly lower enrolment rate than Maaori. These linkage analyses suggest the traditional indicator comparing PHO enrolment and Statistics New Zealand population projections by ethnicity (such as is used in the national Maaori Health Plan indicator) is not a reliable indicator to determine the enrolment rate by ethnicity because of numerator and denominator bias: the inconsistent way that ethnicity was recorded between numerator and denominator. While the analysis reported in this paper ensures that the ethnicity record in both numerator and denominator are identical for an individual, it does not correct for miscategorisation of ethnicity. Some people who are Maaori may be counted as Pacific or NZ European – but if the initial concern is that they are missing out on care because of non-enrolment, given enrolment across all ethnicities is around 98%, it seems unlikely that they are missing out on enrolment advantages whatever group they are classified in. (It is important to differentiate the issue of non-enrolment from the need of having more accurate ethnicity coding in the health sector, which remains).

Counties Manukau District Health Board

System Level Measures

Hospital Standardised Mortality Ratio It is recommended that the Executive Leadership Team review and discuss the following:

1. Analysis and ‘drill down’ of Hospital Standardised Mortality Ratio (HSMR) SLM

2. Contributory factors associated with HSMR for CMH

And provide guidance on:

3. Strategies to improve performance on HSMR Prepared and submitted by Naina Raj, Sybil Hau, Mataroria Lyndon Approved by: Clinical Governance Group 1. Purpose System Level Measures drill down: Hospital Standardised Mortality Ratio

2. Background

The Hospital Standardised Mortality Ratio (HSMR) is a measure of effectiveness including patient safety, and represents the actual verses expected rates of mortality occurring among hospitalised patients, based on patient mix and community variables. The HSMR is a measurement tool that allows hospitals to review and analyse mortality rates, and then develop targeted strategies to reduce mortality in identified areas. The key findings of this analysis are listed below. Key findings

• The HSMR for CMH is 83, which is below the Health Roundtable average of 100, and is approaching the IHI benchmark of 75.

• The HSMR for CMH is lower during weekdays when compared to weekends. • There is no major variation in the HSMR between males and females. It is difficult to

draw any firm conclusions on age and ethnic differences in the HSMR. This is mainly due to the lack of enough data points (ie low number of cases), that is required to detect any significant variation.

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3. Proposal The rationale for reporting on the SLMs is to provide robust information to support progress toward and achievement of the CMH ‘Triple Aim’ of improving population health, patient experience, and cost and productivity. If our system is performing at the highest level, then this will be reflected in those contributory measures which flow into the SLMs. Furthermore, if we could be performing better on an SLM, it is an indication that we should identify how and where processes need to be improved within our system. With this point in mind, it is pertinent that a drill down into hospital standardised mortality rate is completed, with a focus on quality improvement.

Strategic Fit This SLM aligns with our First, Do No Harm executable strategy that is responsible for implementing quality improvement and safety initiatives across our healthcare system and improved quality, safety and experience of care (Aim 2) of the Triple Aim.

1. Definition: The HSMR is calculated as 100 * Observed Deaths / Expected Deaths. An HSMR above 100 means a hospital’s mortality rate is higher than expected given its patient mix. An HSMR below 100 means a hospital’s mortality rate is lower than expected. It is noted here that a higher HSMR needs to be treated with caution due to several possible limitations that can skew the overall ratio. The most commons are:

• The quality of data recorded that includes accuracy of clinical coding. • Availability of hospice beds in the community. Limited beds will increase the number

of people dying in hospital, therefore increasing the HSMR.

2. Methodology (see appendix)

3. How are we performing?

3.1. System Level Measure: Hospital Standardised Mortality Rate

2

The HSMR for CMH is 83, which is below the Health Roundtable average of 100, and is approaching the IHI benchmark of 75. The chart shows common cause variation in the HSMR trend for CMH, between 2010 and 2013.

How are we performing against other similar hospitals?

Health Roundtable HSMR

UCL

LCL

0

20

40

60

80

100

120

140

2010

Jan-

Mar

2010

Apr

-Ju

n20

10 Ju

l-Se

p20

10 O

ct-

Dec

2011

Jan-

Mar

2011

Apr

-Ju

n20

11 Ju

l-Se

p20

11 O

ct-

Dec

2012

Jan-

Mar

2012

Apr

-Ju

n20

12 Ju

l-Se

p20

12 O

ct-

Dec

2013

Jan-

Mar

2013

Apr

-Ju

n20

13 Ju

l-Se

p20

13 O

ct-

Dec

HSM

R Hospital Standardised Mortality Rate (C Chart)

IHI Benchmark

HSMR Average

3

New Zealand HSMR

When compared to both New Zealand HSMR and Health Roundtable (HRT) HSMR, the CMH HSMR is below the average for the 2012-2013. Contributory Measures The following section encompasses a drill down of the CMH HSMR by day of admission, demographic groups, and principal diagnoses, for the time period between July 2008 and December 2013. Control charts are unable to be constructed due to lack of required data points.

3.2. HSMR for weekend admissions compared to HSMR for weekday admissions for emergency episodes between July 2009 and June 2013

The charts below show higher hospital standardised mortality rate over the weekend in contrast to weekdays.

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Weekday Weekend Episodes 179, 329 60,063 Deaths 1,840 695 Expected Deaths 2,008.2 635.4 HSMR 0.92 1.09

Figure 2. HSMR by Day of Admissions July2009-June 2013

Monday Tuesday Wednesday Thursday Friday Saturday Sunday Episodes 36,887 36,349 35,249 35,770 35,074 29,952 30,111 Death 390 347 380 325 398 364 331 Expected Deaths

431 405 386 395 391 318 317

HSMR 0.90 0.86 0.98 0.82 1.02 1.14 1.04

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

Weekday Weekend

HSMR for weekend admissions compared to SMR for weekday admissions July 2009-June 2013

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

HSMR by Day of Admission July 2009-June 2013

Figure 1. HSMR for weekend admissions compared to HSMR for weekday

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Figure 3. Surgical and Medical episodes: SMR for weekend admissions compared to SMR for weekday admissions

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

Medical Surgical

HSMR for weekend admissions compared to SMR for weekday admissions July 2009-June 2013

Weekday Weekend

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

Gastrointestinalsurgery

Stroke Heart Failure Arrhythmia,cardiac arrest

and conductiondisorders

Sepsis # NOF

HSMR for weekend admissions compared to SMR for weekday admissions

Weekday

Weekend

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3.3. Breakdown by gender and age groups

There is no major variation in the overall HSM rate between male and female for all age groups.

Definition for age groups: Younger People= 0-16 years Amongst younger people, the HSMR was higher for females than males during the period 2008-2012. This trend is reversed from the middle of 2012 -2013 Fiscal Year (FY) where the female HSMR declined while there is a steep rise in the male rates (6 male deaths in contrast to 1 female in FY 2013 Jul-Dec period). A further drilldown in FY 2013 Jul-Dec period show ‘Infectious & Parasitic,’ ‘Respiratory System,’ ‘Perinatal’ the three leading contributory factors for higher HSMR in young males at the principal diagnosis level.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

2008-2009 FY 2009-2010 FY 2010-2011 FY 2011-2012 FY 2012-2013 FY 2013 Jul-Dec

HSMR- by gender for all age groups

Female Male Overall

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

2008-2009 FY 2009-2010 FY 2010-2011 FY 2011-2012 FY 2012-2013 FY 2013 Jul-Dec

HSMR-by gender younger people

Female Male Overall

7

Definition for age groups: Adult=17 to 64 There is no major difference seen the in HSM rates between adult males and females.

Definition for age groups: Older people= 65+ Only slight variation is seen between males and females amongst the older age group.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

2008-2009 FY 2009-2010 FY 2010-2011 FY 2011-2012 FY 2012-2013 FY 2013 Jul-Dec

HSMR-by gender adults

Female Male Overall

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

2008-2009 FY 2009-2010 FY 2010-2011 FY 2011-2012 FY 2012-2013 FY 2013 Jul-Dec

HSMR- by gender older people

Female Male Overall

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3.4. Breakdown by ethnicity and principal diagnosis

Definition for age group: Younger People= 0-16 years While the Maori HSMR rates amongst young people are similar to the overall rates for the same age group from FY2008 to 2011, some variation is seen from FY2011 peaking above the overall rate for the same age group in 2013 Jul-Dec. Due to lack of enough data points it is difficult to draw a conclusion whether there is a significant ethnic variation here (ie common cause or special cause variation). A further drill-down indicates Endocrine/Metabolic, Circulatory and Respiratory the three leading contributory factors at the principal diagnosis level.

Definition for age group: Adult=17 to 64 years The HSM rate for adult Maori over the years is similar to the overall rate for the same age group.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

2008-2009 FY 2009-2010 FY 2010-2011 FY 2011-2012 FY 2012-2013 FY 2013 Jul-Dec

HSMR-Younger People

Maori Overall

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

2008-2009FY

2009-2010FY

2010-2011FY

2011-2012FY

2012-2013FY

2013 Jul-Dec

HSMR-Adults

Maori Overall

9

Definition for age group: Older people= 65+ 4. Future Steps This document has been provided as a conversation starter for the HSMR System Level Measure. Expert feedback is required from the Executive Leadership Team to ensure that definitions, approaches and contributory measures are relevant and informative. We also seek guidance from the ELT on potential strategies to improve performance on HSMR.

5. Appendix Methodology Name Hospital Standardised Mortality Ratio Description The ratio of the actual number of in-hospital deaths to the expected rate of

deaths based on the types of patients a region or hospital treats. Rationale

The HSMR is a tool that allows hospitals to review and analyse their mortality rates, and then develop targeted strategies aimed at reducing mortality in identified areas.

Domain(s) of quality Effective

Type of measure Outcome Proposed indicator status Improvement

Numerator Observed mortality (number of deaths reported for the hospital) Denominator Expected mortality (number of deaths that the analysis predicts based on the

casemix of the hospital) Source (s) Inpatient coded data – Health Roundtable Contact person (people)

Decision Support

Reporting 6 monthly

0.000.200.400.600.801.001.201.401.601.80

2008-2009 FY 2009-2010 FY 2010-2011 FY 2011-2012 FY 2012-2013 FY 2013 Jul-Dec

HSMR-Older People

Maori Overall

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Benchmark Ratio of 75. Set by the IHI as 25 points below national average (Health Roundtable average is 100). Based on best-known performance seen by IHI, and top-decile performance in national measures (US data).(5)

Person/role responsible for reporting Decision Support

Limitations The HSMR is considered a useful screening tool of adverse events, however, it is highly reliant on the accuracy of administrative coding. Major differences in palliative care service availability can also contribute to wide variation in mortality rates.

Factors adjusted for the calculation of expected deaths for HSMR:

• ICD 10 Code for Principal diagnosis • Gender • Age • Admission Type (Emergency/planned) • Transfer In (Patients transferred in from another acute hospital are distinguished from regular

admissions. • Charlson Comorbidity Index (CCI scores 17 different comorbidities that impact risk of death. This is

truncated to scores 0, 1-2, 3+ Definitions for terms in HSMR for weekend admissions compared to HSMR for weekday admissions Surgical episodes are defined as those with a DRG in the surgical partition of DRG6.0x Medical episodes are defined as those with a DRG in the medical partition of DRG6.0x Gastrointestinal surgery is defined as episodes with a DRG in Clinical Service Group 10 (GI Surgery) Stroke is defined as any of the following ICD10 principal diagnosis codes: I61, I629,

I63, I64 Heart Failure is defined as ICD10 principal diagnosis code I50 Arrhythmia, cardiac arrest is defined as episodes in DRG F76 as per DRG version 6.0x and conduction disorders: Sepsis is defined as ICD10 principal diagnosis code A40.x or A41.x #NOF is defined as ICD10 principal diagnosis code S72

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