The 8 goals RACS have announced to stamp out bullying

The Royal Australasian College of Surgeons has launched an Action Plan to turn around the reputation of their profession.

The intention of their plan is to ‘promote respect, counter discrimination, bullying and sexual harassment in the practice of surgery, and improve patient safety’.

The Action Plan is a response to a draft report and recommendations released by an Expert Advisory Group commissioned by RACS in response to reports of bullying behaviour in the surgical field.

The report found there is culture of bullying that is considered a ‘rite of passage’ within the College with the intent to prepare trainees for surgery.

There are 8 goals in the Action Plan with a set of actions for each one to help the college monitor its progress.

The goals are:

Build a culture of respect and collaboration in surgical practice and education
Respecting the rich history of the surgical profession, advance the culture of surgical practice so there is no place for discrimination, bullying and sexual harassment (DBSH)
Build and foster relationships of trust, confidence and cooperation on DBSH issues with employers, governments and their agencies in all jurisdictions
Embrace diversity and foster gender equity
Increase transparency, independent scrutiny and external accountability in College activities
Improve the capability of all surgeons involved in surgical education to provide effective surgical education based on the principles of respect, transparency and professionalism
Train all Fellows, Trainees and International Medical Graduates to build and consolidate professionalism including: fostering respect and good behaviour, understanding DBSH: legal obligations and liabilities, ‘calling it out’/not walking past bad behaviour and resilience in maintaining professional behaviour
Revise and strengthen RACS complaints management process, increasing external scrutiny and demonstrating best practice complaints management that is transparent, robust and fair

Read the full action plan on the RACS website.

So far, responses from doctors have been positive. Dr Ashleigh Witt, who gained prominence in the mainstream media earlier this year for her comments on being a female surgeon, tweeted ‘I’ve only skimmed but so far very impressed by this. Excellent work @RACSurgeons #bullyinginmed‘

St Vincent’s Health tweeted ‘Congratulations @RACSurgeons on this important announcement. We stand ready to work with you on its implementation.’

Senior surgeon and author on gender equality Dr Gabrielle McMullin told ABC Radio that she wasn’t sure it would change anything: “The problem is getting people, trainees, to believe that a complaint will not adversely affect their progression in their career and getting a job. That is the major reason why trainees don’t complain about harassment and bullying, is because you lose your career if you complain.”

Tells us what you think on our Facebook page.

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Who are you? 7 facts about the average doctor in Australia

An annual workforce report by the Australian Institute of Health and Welfare has provided a statistical snapshot of medical practitioners in Australia.

The AIHW uses survey data from APHRA about the 98,807 medical practitioners registered in 2014, which has increased by 7.4% in two years.

Other key facts are:

1. A third of medical practitioners are GPs

In the last 10 years, there has been a steady rate of supply of general practitioners, with 111 per 100,000 population in 2014. There were 32,606 registered GPs in 2014, making up 33% of medical practitioners in Australia.

2. There are more specialists now than 10 years ago

In the last 10 years there has been growth in the rate of specialist supply, from 110 to 132 per 100,000 population. Specialists working as clinicians increased from 19,043 in 2004 to 28,403 in 2014.

3. Anaesthesia is the most common speciality

The five most common specialities account for 38.7% of clinician specialists. Anaesthesia is the most common with 3,775 or 13.3% of clinician specialists followed by psychiatry, Diagnostic radiology, General surgery and Specialist obstetrician and gynaecologist.

4. The number of female doctors is increasing

The proportion of women employed as medical practitioners has increased steadily in the past 10 years. In 2014, women made up 39.4% of the medical workforce. There are substantially more men in the older age groups and more women than men in the 20-34 age group.

Graph: AIHW

5. Average age gap between men and women is decreasing

The average age of men is 48 in 2014 and has been relatively steady since 2004. The average age for women is 42 in 2014 however the average age gap over this period has narrowed slightly from 6.8 years in 2004 to 6.1 years in 2014.

6. Working hours have remained steady but on average, men work longer

The report found that medical practitioners work an average of 42.5 hours per week, which has remained steady since 2010. Men work on average 45.1 hour and women work on average 38.6 hours per week.

7. About a third of medical practitioners gained their qualifications overseas

66.4% of employed medical practitioners said they obtained their initial medical qualification in Australia. Among those who obtained their qualification overseas, those who qualified in India was the largest group followed by England and New Zealand.

Read more of the report on the AIHW website.

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Don’t just sign on the dotted line: assessing fitness to drive

GP Dr Genevieve Yates shares “Don’t just sign on the dotted line: assessing fitness to drive”, a Pecha Kucha talk (6min 40sec talk comprising of 20 slides, each lasting 20 seconds) reminding us that driving is a privilege, not a right.

In July 2015, while visiting friends in LA, my partner was hit and killed while out jogging.

Since he died, I have been trying to find things that are positive and helpful to make a very horrible situation feel a little less senseless. One of these is by raising awareness of the dangers of unfit drivers on the road.

I hope that by sharing my personal story in this way, it might just result, indirectly at least, in someone’s husband, wife or child being spared.

Nothing can bring the love of my life back. But if sharing our story indirectly results in one fewer person being injured by an unfit driver, at least some good has come out of this senseless tragedy.

Dr Genevieve Yates is a doctor and medical educator from regional Australia. Read her blog here and follow her on twitter. If you work in healthcare and have a blog topic you would like to write for doctorportal, please get in touch.

GPs in ‘unique position’ to help domestic violence victims

On International Day for the Elimination of Violence against Women, GPs are being reminded of their unique position at being one of the first people a victim may turn to.

RACGP President Dr Frank R Jones said GPs need to understand the nature of violence and abuse to help break the cycle.

“This includes identifying predisposing risk factors, understanding early signs and symptoms and managing the devastating consequences of family violence.”

The RACGP is one of a group of Australia’s peak medical bodies that have joined forces to help end domestic violence.

22 Colleges and peak health bodies issued a joint statement saying they will be wearing white ribbons in their workplaces and they will ‘indicate their willingness and availability to discuss this sensitive and difficult issue, should they be experiencing violence in their lives.’

Domestic violence tools

The RACGP’s white book Abuse and violence: working with our patients in general practice gives doctors evidence-based guideline on identifying domestic violence and how to respond. This edition also offers new insights into Aboriginal and Torres Strait Islander people as well as migrant, refugee and rural communities.
The National Sexual Assault, Domestic Family Violence Counselling Service 1800RESPECT last week launched a new toolkit to help GPs better recognise the signs of assault and empower them to respond. Visit 1800RESPECT to order your kit.
The AMA/Law Council of Australia document Supporting Patients Experiencing Family Violence kit contains information about specialist support services, including health, mental health, drug and alcohol, legal, family support and child protection services.
Victim support services and further reading on the AMA website.

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‘We are professional on social media’ medical students say

The Australian Medical Students’ Association has hit back at claims that a third of medical students post inappropriate material to their social media accounts.

AMSA was responding to a recent survey, published online today in the Medical Journal of Australia, that found 34.7% of respondents reported posting unprofessional content in their social media accounts. Intoxication was the number one ‘inappropriate’ posting, followed by illegal drug use and posting of patient information.

AMSA President James Lawler said he was proud of their members and the professionalism they display on social media.

“AMSA has played a leadership role in giving students clear advice on how to manage their engagement with social media and believes the overwhelming majority of students are acting in a professional and responsible way.

“The MJA study clearly has a number of limitations in its methodology.

“While it makes a contribution to the debate over social media, its results need to be interpreted with caution.”

880 students voluntarily completed the survey over 6 months in 2013.

The authors of the paper, Drs Christopher Barlow and Stewart Morrison from The Alfred and St Vincent’s, acknowledged the limitations of the study, including that it included a small proportion of the 16 993 medical students enrolled that year.They also said most of the participants were from a small number of universities which may limit the generalisation of the results. The survey also relied on self reporting and recruitment was done on social media.

35% of respondents changed their social media privacy settings as a result of the survey, suggesting that education and reminders could be a simple and effective intervention.

Mr Lawler said that social media is an important communication tool and shouldn’t be demonised.

“There are also a range of benefits from social media in medical education, such as the Free Open Access Medical Education movement ( #FOAMed).

“AMSA will continue to work closely with medical students to maximise the benefits of social media in their studies, on the path to a medical career.”

AMSA and the AMA created guidelines in 2010 for the professional use of social media for doctors and medical students.

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The treatment of nursing home-acquired pneumonia using a medically intensive Hospital in the Home service

Nursing home-acquired pneumonia (NHAP) is a common cause of admissions to hospital of people living in residential aged care facilities (RACFs); most studies have found that it accounts for the majority of non-trauma-related acute hospital admissions in this population.1–9 Dementia, immobility, swallowing problems, diabetes and renal impairment are typical comorbidities;9–11 disease severity on presentation is therefore typically greater than for community-acquired pneumonia, and in-hospital and 30-day mortality rates are high, ranging from 10% to 40%.4,9–15 NHAP requires significant inpatient resources, as the hospital length of stay for these admissions is higher than for community-acquired pneumonia, with published mean stays of between 7.0 and 18.7 days.9–15 Morbidity is also high in this group of patients, who are exposed to increased rates of unintended harm from complications such as falls and pressure wounds.9–17

Nevertheless, most NHAP patients are treated successfully and discharged back to their usual care.7,8,18 Despite growing concerns, most respiratory infections in this group do not involve treatment-resistant organisms.9–15

Some studies suggest that NHAP mortality does not differ between those treated in nursing homes and those transferred to hospital.19,20 However, these studies may not adequately match patients with regard to severity of disease, and it is difficult to ascertain the level of intervention delivered to NHAP patients.21,22 Oral therapy (antibiotics, rehydration) is unlikely to be tolerated by an unwell RACF patient with pneumonia.

Families and patients accept alternatives to hospitalisation that deliver similar care to hospitals.23 Hospital in the Home (HITH) has been established in Victoria for 20 years. This model of care has been shown to be safe and efficacious, including by a randomised study of mild to moderately severe community-acquired pneumonia.24,25 Its use has also been described in the acute management of older patients, and of patients in nursing homes.26–28 However, we could find no published study that specifically examined the safety and outcome of NHAP treated in a medically managed HITH care model. In July 2013, the Royal Melbourne Hospital established a mobile x-ray service that enhanced the capacity of the HITH unit to diagnose and treat patients with suspected significant lower respiratory tract infections in their nursing homes.

The aim of this study was to compare the clinical characteristics, intravenous therapy, treatment safety and outcomes of NHAP treated solely by HITH in the nursing home setting with those of conventional in-hospital treatment.

Methods

The Hospital in the Home intervention model

The Royal Melbourne Hospital HITH service is responsible for all acute medical and pharmaceutical care, and acute nursing, pathology and radiology services for its patients, who remain inpatients of the hospital during their HITH stay. The HITH model provides a 7-day, 24-hour service, can administer oxygen and intravenous antibiotics and fluids, and includes pathology and mobile radiology services. Medical staff visit the patient in their residential home or facility each day, and one or two daily nursing visits are also made.

Study population

This study was a case–control study of patients in high-level RACFs who had been admitted to the Royal Melbourne Hospital for NHAP between 1 July 2013 and 31 January 2014. Patients who had been treated in hospital for more than 48 hours before their admission to the HITH unit were excluded. The study population included patients treated for acute lower respiratory tract infection (pneumonia, aspiration pneumonia, infective exacerbation of chronic obstructive pulmonary disease, infective exacerbation of bronchiectasis), as identified in the database of the HITH unit. The control group included patients with the same diagnoses, but admitted to hospital for conventional care during the study period; they were identified by coding and patient address data recorded by the Health Information Service of the Royal Melbourne Hospital.

Data

Data were obtained from hospital medical records, pathology and radiology systems, and the mobile x-ray service. Patient demographic (age, sex), comorbidity (age-adjusted Charlson comorbidity index), functional status (modified Barthel index) and Mini-Mental State Examination data (if available) were collected. Clinical features were described using the McGeer criteria, and the following laboratory data collected: white blood cell count, haematocrit, C-reactive protein, electrolyte and blood gas levels, and microbiological findings. Pneumonia severity scores (CURB-65, CORB) were calculated from clinical observations and pathology at admission. Information on antibiotic allergies, dehydration, acute confusion, intensive care unit admission, inotropic support, non-invasive ventilation, active resuscitation attempts and advanced care directives was also obtained from records for patients in both groups.

Treatment information gathered from hospital drug charts included the requirement for intravenous fluids or supplemental oxygen, and the duration of intravenous antibiotic therapy. An allergy mismatch was recorded when a patient was prescribed a drug to which an allergy had been noted in their record.

Outcome measures

The primary outcome measure was hospital length of stay. Acute length of stay refers to the stay in the acute ward or HITH; total length of stay refers to the total hospital stay, including acute care and any in-hospital rehabilitation or assessment, such as geriatric evaluation and management.

Secondary outcome measures included 30-day mortality, inpatient mortality, complications, and readmission to hospital within 30 days. These data were obtained from the medical records of the hospital and the RACFs. Complications were classified as treatment-related (antibiotic drug-related adverse events, catheter-related infections) or hospitalisation-related (reported falls, new pressure wounds).

Unplanned interruption refers to a resumed admission to the traditional ward during the HITH admission, where such a return had not been expected or arranged for a procedure or other intervention. This is a clinical quality indicator that is routinely collected by the HITH.

Statistical analyses

This study was designed a priori as an equivalence analysis. It was powered to explore the hypothesis that there was no difference between the length of stay in either direction for HITH and control patients, presuming an equivalence limit of 4.5 days. This limit was selected on the basis of published studies which suggest that the mean length of stay for traditional hospital admissions for NHAP ranges between 7.0 and 18.7 days.10–18 Using the conservative estimate of a standard deviation of 8 days, derived from the same papers and our own pilot data, it was estimated that a minimum sample size of 52 patients per group was required to attain a power of 80% for excluding a difference in mean length of stay of greater than 5 days, based on non-overlap of two-sided confidence intervals.

Categorical variables were summarised as frequencies and percentages, and compared using χ² or Fisher exact tests as appropriate. Continuous variables were summarised as means and SDs or medians and interquartile ranges (IQRs), and compared using t– or Wilcoxon rank-sum tests as appropriate. Length of stay data were compared using median quantile regression, fully adjusted for baseline differences. Logistic regression was used to study 30-day mortality and readmission by group, adjusted for baseline differences. Appropriate goodness-of-fit tests were conducted for regression models.29 For all analyses, P < 0.05 was defined as statistically significant. All analyses were conducted with Stata 13 (StataCorp).

Ethics approval

Approval for the study was obtained from Royal Melbourne Hospital Ethics Committee (reference 2014050).

Results

Sixty HITH patients and 54 hospital control patients were identified during the study period. The HITH patients were older (P = 0.042), were less likely to have an advanced care plan at the time of treatment (P < 0.001), were more likely to be dehydrated (P = 0.06), and were less likely to have received non-invasive ventilation support (P = 0.01); the group included more men than women (P = 0.026) (Box 1). There were no other significant differences in baseline characteristics between the two groups. Thirty-two patients (53%) were admitted directly into HITH care without a hospital or emergency department stay; 25 (42%) had been referred directly from the emergency department, and three (5%) were referred from the hospital’s rapid medical assessment unit.

Length of stay

There was no difference between the median lengths of stay of HITH and control patients after adjusting for baseline differences (median regression coefficient, −1.00 days; 95% CI, −2.72 to 0.72; P = 0.252).

Management of NHAP

There was no significant difference between the proportions of patients in the two groups for whom blood tests and chest x-rays were undertaken. Microbiological specimens were submitted for a significantly greater proportion of patients in the control group (P < 0.001), but there was no difference in the rate of detection of specific respiratory pathogens. Similar proportions of patients in the two groups were given intravenous fluids and supplemental oxygen. Non-invasive ventilation was more likely to be used in the control group (it was not used for HITH patients). No patient from either group was mechanically ventilated. The duration of intravenous antibiotic treatment before switching to oral antibiotics was significantly longer for HITH patients (P < 0.001) (Box 2).

The pattern of antibiotic prescribing is summarised in Box 3. HITH patients received ceftriaxone and moxifloxacin more often than control patients, who were more likely to receive azithromycin and metronidazole.

The frequency of antibiotic allergy mismatch in the two groups was similar. All except one of the mismatch cases involved mild or non-life-threatening mismatches. There were no reported adverse reactions associated with antibiotic treatment.

Mortality

There were no significant differences in overall mortality at 30 days for the two groups, after adjusting for baseline differences (adjusted odds ratio [aOR] for HITH v control patients, 1.97; 95% CI, 0.67–5.73). Inpatient mortality for HITH patients was lower (aOR, 0.19; 95% CI, 0.05–0.75), but unadjusted postdischarge 30-day mortality was higher than for the control group (OR, 13.25; 95% CI, 1.67–105.75). The number of deaths by 30 days after discharge (12 HITH patients, one control patient), however, limited logistic modelling to unadjusted regression, as indicated by the broad confidence interval. There were no differences between the two groups with regard to complications and 30-day readmission rates after adjusting for baseline differences (aOR, 1.59; 95% CI, 0.30–8.53) (Box 4).

One HITH patient had to return to hospital because the nursing home withdrew their consent for HITH treatment, despite the continuing consent of the family.

Discussion

This study describes the differences between the treatment of NHAP in a traditional hospital setting and when wholly managed in a medically intense HITH setting supported by a mobile x-ray service. There was evidence that the severity of the HITH cases of NHAP was similar to that of cases admitted to hospital with this condition, and our results suggest that the length of stay for these two treatment options may be comparable. It was also found that, after adjustment for baseline differences, there were no significant differences between the 30-day mortality rates and those of readmission to hospital for the two approaches. This suggests that the HITH model is effective and safe for this group of patients. The outcomes we report are consistent with those reported in the international literature.

The median length of stay (reported instead of the mean because of the skewed nature of the data) was shorter than expected in both groups, but the mean total hospital length of stay for the control group was within the range reported for NHAP by other studies.9–15

There are several possible explanations for the finding that in-hospital mortality among HITH patients was lower. First, traditional hospitals may be more forceful in applying rigid limits to the duration of treatment, withdrawing active therapy earlier than in the HITH setting. Prolonging intravenous antibiotic treatment for HITH cases may improve short-term clinical responses. Second, the hospital generally provides palliation services until the death of the patient, while HITH patients were often discharged to their usual care providers after active treatment was stopped and palliation had begun. Finally, the process of transfer to hospital may accelerate clinical deterioration in this frail group.

There are potential challenges to the validity of our findings. Despite the matching of patients and adjustment for baseline differences, this was not a randomised study. We cannot, therefore, exclude the possibility that the two groups differed with regard to other variables, known or unknown, that could account for our findings. Selection of a patient into the HITH group may have been based on the awareness of HITH on the part of the attending doctor or the referring nursing home, the wishes of the patient or their family, or may have been influenced by the availability of a hospital bed at the time of presentation. The reasons for not transferring a patient to HITH in this study could include family reluctance to consider an alternative to traditional hospital care; the unwillingness of the patient’s RACF to accept HITH, or a lack of awareness or will on the part of the treating emergency department doctors of HITH; further unknown or unmeasured markers of disease severity; or the time of presentation. The cluster of RACFs that allowed HITH may have influenced the outcomes (although only one facility refused ongoing HITH, and then only once). These factors may all have influenced the outcomes measured by our study, and caution is required in their interpretation.

HITH offers a partnership in which the RACF provides the sick resident with the usual supportive care, while the HITH provides acute hospital-level intervention. A high level of care was provided: daily medical visits from doctors employed by the hospital; in most cases, twice daily visits by nurses; intravenous fluids and antibiotic treatment, blood tests and x-rays, and 24-hour cover. This study found only one episode where HITH care was terminated by the nursing home before completion, and none in which it was ended by the family. This might suggest that families and the RACFs found the intervention acceptable, but we did not specifically measure satisfaction in this study.

The global impact of NHAP is sobering: there are more than 4 million cases annually, at a median rate of 1.0–3.2 cases per 1000 bed-days and 600 000 emergency admissions.9,11 In 2013, the Australian Institute of Health and Welfare found that permanent RACF patients accounted for 31 760 acute hospital admissions for pneumonia in Australia during the 2008–09 financial year.30 The authors of the report assumed a mean hospital length of stay of 7 days for pneumonia, which amounts to 222 320 hospital bed-days for 2008–09, or 609 fully occupied Australian hospital beds for patients with NHAP. The potential impact of moving some of those patients into HITH care would be substantial.

It is a challenge for hospitals to reconsider the best place to deliver acute care for specific segments of the population. Simply reducing hospital stays for severely unwell patients is not always acceptable. Systems are designed around traditional admission procedures, and alternatives are often not sufficiently prominent or adequately resourced to make a significant impression on the usual processes. However, the proportion of the hospital workload associated with treating RACF patients will only increase. HITH may provide a targeted and effective hospital response that can deliver equivalent quality care without extending the patient’s length of stay. This requires well resourced, intensive, medically based HITH, supported by hospital-level technologies, such as intravenous therapies, expert staff and mobile x-ray facilities, as well as the willingness to meet the challenge of switching care models for the high level of disease severity with which these patients inevitably present.

Box 1 –
Demographic and baseline characteristics of patients in the Hospital in the Home and control groups

	Hospital in the Home patients	Control patients	P

Number of patients	60	54
Median age (IQR), years	86.5 (83–92)	84 (77–98)	0.042
Sex, male	37 (62%)	22 (41%)	0.026
Antibiotic allergy	17 (28%)	17 (31%)	0.714
Median age-adjusted Charlson comorbidity index score (IQR)	7 (6–8.5)	7 (5–8)	0.535
Advance care directives	27 (45%)	42 (78%)	< 0.001
Diagnosis^∗
Pneumonia/acute lower respiratory tract infection	50 (83%)	39 (72%)	0.152
Aspiration pneumonia	10 (17%)	15 (28%)	0.152
Chronic obstructive pulmonary disease, infective exacerbation	7 (12%)	9 (17%)	0.443
CURB-65 score ≥ 3^†	41 (68%)	41 (76%)	0.368
CORB score ≥ 2^‡	36 (60%)	41 (76%)	0.070
Confusion	43 (72%)	39 (72%)	0.947
Dehydration	40 (67%)	22 (41%)	0.006
Fulfilled McGeer criteria
Pneumonia	32 (53%)	30 (56%)	0.812
Lower respiratory tract infection	27 (45%)	22 (41%)	0.646
Pneumonia/lower respiratory tract infection	59 (98%)	52 (96%)	0.498

IQR = interquartile range. * Patients could be assigned to more than one diagnostic category. † Three of: confusion; urea >7 mmol/L; respiration >30/min; blood pressure <90 mmHg; age >65 years. ‡ Two of: confusion (acute); oxygen saturation ≤90%; respiration >30/min; blood pressure <90 mmHg (systolic) or <60 mmHg (diastolic).

Box 2 –
Investigation and management of patients with nursing home-acquired pneumonia

	Hospital in the Home patients	Control patients	P

Number of patients	60	54
Blood tests	57 (95%)	53 (98%)	0.620
Radiological investigations
Chest x-ray	51 (85%)	52 (96%)	0.057
Presence of consolidation	32 (53%)	31 (57%)	0.662
Microbiological investigations
Microbiological specimen sent	25 (42%)	39 (72%)	0.001
Respiratory specimen sent	9 (15%)	17 (32%)	0.036
Respiratory pathogens identified	6 (10%)	6 (11%)	0.847
Intravenous fluids	50 (83%)	40 (74%)	0.226
Oxygen supplementation	41 (68%)	44 (82%)	0.108
Non-invasive ventilation support	0	6 (11%)	0.010
Median duration (range) of intravenous antibiotic treatment before switch to oral treatment, days	4 (0–12)	2 (0–5)	< 0.001
Allergy mismatch	4 (7%)	4 (7%)	1.000
Intensive care unit admission	0	0	NA
Inotropic support	0	0	NA
Resuscitation	0	0	NA

NA = not applicable.

Box 3 –
Drug selection for initial treatment of patients with nursing home-acquired pneumonia

Drug	Hospital in the Home patients	Control patients	P

Ceftriaxone	54 (58%)	41 (44%)	0.044
Azithromycin	14 (15%)	23 (25%)	0.028
Moxifloxacin	12 (13%)	1 (1%)	0.002
Metronidazole	9 (10%)	15 (16%)	0.095
Benzyl-penicillin	1 (1%)	5 (5%)	0.100
Gentamicin	1 (1%)	0	1.000
Vancomycin	1 (1%)	3 (3%)	0.343
Cefazolin	0	1 (1%)	0.474
Clindamycin	0	1 (1%)	0.474
Piperacillin/tazobactam	0	2 (2%)	0.222
Meropenem	0	1 (1%)	0.474

Total prescriptions	93	92

Box 4 –
Outcomes for patients with nursing home-acquired pneumonia

	Hospital in the Home patients	Control patients	P

Number of patients	60	54
Median acute care length of stay (range), days	4 (1–12)	4 (1–28)	0.959
Total length of stay (range), days	4 (1–12)	4 (1–81)	0.841
Mean total length of stay in hospital, days	5	10
Complications
Antibiotic-related	0	2 (3.7%)	0.222
Catheter-related	0	0	NA
Falls	0	2 (3.7%)	0.222
Pressure wounds	0	0	NA
Mortality
Inpatient	4 (6.7%)	17 (31.5%)	0.001
30-day	12 (20%)	1 (1.9%)	0.002
Overall in 30 days	16 (26.7%)	18 (33.3%)	0.437
Readmission within 30 days	6 (10%)	3 (5.6%)	0.496
Unplanned hospital admission	1 (1.7%)	NA	NA

NA = not applicable.

The challenge for GPs: potential early cancer diagnosis vs over testing

General Practitioners face a balancing act when trying to trying to rule out an early cancer diagnosis in their patients.

Professor Jon Emery, the Herman Professor of Primary Care Cancer Research at the University of Melbourne wrote in the Medical Journal of Australia that GPs might only see 5-10 cases of non-cutaneous cancer each year among their several thousand consultations.

As a result, “even red-flag cancer symptoms have low positive predictive values”, he wrote.

“Only a few symptoms, such as [coughing up blood], breast lump and [blood in the urine], have a greater than 5% chance of being due to cancer in primary care. Most symptoms of cancer have more common benign causes in general practice. Further, cancers in general practice often present initially with more subtle non-specific symptoms.”

He said GPs are faced with the pressure of over-investigating patients who are unlikely to have cancer and the resultant costs to the patient and the health care system.

GPs also have limited access to key tests which leads them to order less appropriate ones.

There are a new range of risk assessment tools (RATs) such as the charts developed by Hamilton and colleagues and the QCancer model for men and women of Hippisley-Cox and Coupland.

However Professor Emery said “there is limited evidence on how GPs will use such tools or what impact they will have on diagnostic decision making”.

Read the full article in Medical Journal of Australia

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Do teleoncology models of care enable safe delivery of chemotherapy in rural towns?

Even in developed nations, cancer survival rates among patients from rural regions are often inferior to those of their urban counterparts.1–3 In Australia, these problems are further compounded by the poorer outcomes for Indigenous patients compared with non-Indigenous patients.4 Reasons that have been proposed to explain this disparity include the differential access of rural and urban patients to various cancer screening and treatment programs.5 Achieving timely and equitable access to cancer care services for all patients remains a significant challenge, especially in large countries with geographically dispersed populations, such as Australia.

When compared with their urban counterparts, rural patients in New South Wales have different rates of prostatectomy and orchiectomy for prostate cancer,6 undergo less breast-conserving surgery for breast cancer,7 and have a lower probability of completing radiotherapy for rectal cancer.8 Overseas studies have also reported that the uptake of chemotherapy may be lower for patients from rural areas; for example, patients with colorectal cancer living in disadvantaged areas of Scotland were less likely to receive chemotherapy.9 A Canadian study by the British Columbia Cancer Agency similarly reported that patients from rural areas were less likely to receive adjuvant chemotherapy for rectal cancer than those from larger urban centres.10

There are many possible explanations for the differing rates of chemotherapy in rural and urban populations. These include the limited access to chemotherapy closer to home5 and clinicians being concerned about the potential toxicity of chemotherapy. To explore the latter possibility, the Townsville Cancer Centre (TCC) conducted a study of patients with breast and colon cancer in northern Queensland.11 Its findings suggested that rural patients with these cancers could be treated safely with the same doses and dose intensity as their urban counterparts; further, complications of treatment could be managed at rural centres, with supervision and partnership shared by rural and urban clinicians.11

A possible solution that would provide timely access to chemotherapy closer to home and improve the uptake of chemotherapy by rural and remote patients would be to administer chemotherapy in rural centres, with medical oncology support and supervision provided through teleoncology (telehealth for cancer care). Similar to cancer centres in Kansas (United States) and Kelowna (Canada),12,13 medical oncologists from our centre, the TCC, supervise the delivery of chemotherapy agents in Mount Isa (a large rural town 900 km from Townsville) using the Townsville teleoncology model that operates under the auspices of the Townsville Teleoncology Network (TTN).14,15 Patients in Mount Isa are able to receive almost all types of solid tumour chemotherapy. Within the TTN, medical oncologists are able to assess rural patients for fitness to undergo chemotherapy and to use video-conferencing to make decisions about admitted inpatients. This assessment is supported by rurally based doctors and nurses during telehealth consultations. Chemotherapy-proficient nurses administer chemotherapy agents prescribed by TCC-based medical oncologists.

Although this model has been shown to be accepted by both patients and health professionals,16 and facilitates timely provision of medical oncology services in rural towns,17 it is not known whether the safety and quality of treatment received by Mount Isa patients (as indicated by dose intensity and toxicity profile) are comparable with those for Townsville patients. The aim of this study was therefore to determine whether there were any differences between the quality and safety of chemotherapy received by patients treated in person at the TCC and those treated at the Mount Isa Hospital by the same oncologists via teleoncology.

Methods

Data collection

Retrospective chart audits were conducted at both the Mount Isa Hospital and the TCC for patients who received chemotherapy. The data collected included:

demographic details, including age, sex and cancer type;
types of chemotherapy regimen, dose intensity (actual and planned doses) and number of treatment cycles;
intent of treatment: curative (chemotherapy was the primary therapy or an adjunct to surgery that aimed to cure cancer) or palliative (chemotherapy that aimed to prolong survival and to improve or maintain quality of life); and
rates of severe side effects (grade 3 and 4 toxicities according to National Cancer Institute Common Toxicity Criteria [NCI CTC], version 4)18 and of admissions to inpatient facilities linked with the side effects of cancer therapy.

Patient selection

The Mount Isa audit included all chemotherapy administered from the inception of the Townsville teleoncology model of care from 1 May 2007 until 30 April 2012. The TCC audit was conducted during two separate 3-month periods: March – May 2009 and June – August 2010. These two periods were chosen for two reasons. First, from 2010 many patients were enrolled in clinical trials at the TCC, a tertiary centre, and these trials were not available at Mount Isa; including these TCC patients would make comparing the data difficult. Second, referral patterns at the TCC fluctuate during major holiday seasons according to the availability of surgical theatres; the end-of-year holiday period was excluded from our study because of the unusual patient profile at the TCC at this time of year. The two unrelated time periods for data collection were thus selected to avoid tumour selection bias in the study population. As Mount Isa does not have radiotherapy facilities, patients undergoing chemoradiotherapy at the TCC were also excluded from the study.

We also attempted to match the sample population for patient comorbidities, but chart data on minor comorbidities tend to be incomplete. It was therefore assumed that both patient populations were fit for chemotherapy, based on the usual practice that patients with severe comorbidities and poor performance status would not be offered chemotherapy.

Statistical analysis

The SPSS program (IBM) was used for all analyses. Between-group differences for categorical variables were analysed with χ² tests; where the expected cell count was less than 5, the Fisher exact test was instead used. Between-group differences for numerical variables were analysed with t tests; where data were not normally distributed or the sample size for each group was less than 30, Mann–Whitney U tests were used instead. Statistical significance was defined as P < 0.05. Sample size calculations indicated that a total of about 160 participants was required to detect a between-group difference of 20% in the rate of side effects, assuming a base side effect rate of 10% (previously determined at Townsville Hospital), with 90% power and α = 0.05.

Ethics approval

This study received ethics approval from the human research ethics committees of the Townsville Health and Hospital Services (HREC/12/QTHS/29) and the James Cook University (H4602).

Results

During the period May 2007 – April 2012, a total of 89 patients received chemotherapy at Mount Isa Hospital under the supervision of TCC medical oncologists through the teleoncology model. The comparison group included 117 eligible patients from Townsville. Demographic details are summarised in Box 1. The three most common cancer types were breast, colorectal and lung cancers, although most solid tumour types were treated at both sites. There were no significant differences in the characteristics of the patients at the two sites with respect to sex, age, cancer types or treatment intent (P > 0.05 for each comparison). However, significantly more Indigenous patients were treated at Mount Isa than in Townsville (χ² [1] = 11.66, P < 0.001).

Chemotherapy doses and side effects

A total of 626 and 799 cycles were respectively administered at the Mount Isa and Townsville hospitals. All chemotherapy regimen types (lines) used in Townsville were also available to patients in Mount Isa, but as the number of patients receiving each regimen type was small, no comparison between Mount Isa and Townsville was attempted in this regard. Data on the chemotherapy cycles and toxicity rates are summarised in Box 2 and Box 3; the side effect profiles at the two hospitals are summarised in Box 4.

No statistically significant differences between the hospitals were observed with regard to the numbers of treatment cycles, of cycles per line, of lines per patient, of side effects, or of hospital admissions (P > 0.05 for each comparison). Although neutropenia was reported more frequently in Mount Isa, this did not cause more hospital admissions or dose delays. There were no deaths in either group caused by toxicity. Further, there were no statistically significant differences in dose intensities between sites, regardless of treatment intent. Due to the higher proportion of Indigenous patients in Mount Isa, the analysis comparing sites was stratified by Indigenous status; no site differences in any parameters related to dose intensities and rates of serious adverse events were detected after this stratification.

Discussion

Teleoncology models enable many types of chemotherapy to be administered in a timely manner closer to home for rural patients, with close supervision by medical oncologists from urban centres.14,17 This model of remote chemotherapy supervision has been shown (a) to reduce the need for rural patients to travel long distances; (b) to be accepted by both patients and health professionals; and (c) to reduce health care system expenses.12,19 However, it is also imperative to ensure that safety is not compromised, and that the quality of care provided through these models is of at least the same standard as that experienced by patients receiving their care from oncologists in person.

It had previously been reported that thrombolysis could be safely and effectively performed on stroke patients at remote centres using telehealth techniques.20 Our study has shown that, in comparable populations, there were no statistical differences in safety parameters between an urban, traditional model of care and a rural teleoncology model. Similar numbers of treatment cycles and lines and dose intensities indicate that the administration of therapy was comparable for the rural and urban patient groups. Although the Mount Isa group included a greater number of Indigenous patients, it did not affect our results, as chemotherapy treatment decisions are based on medical comorbidities and not on ethnic background.

Our study is the first to show that many types of chemotherapy can be administered in rural centres, without compromising safety and quality, by teleoncology models of care. These results, together with those of an earlier study that compared the safety of chemotherapy for rural and urban patients with breast or colon cancer,11 may reassure many urban clinicians that high-quality cancer care can be provided at rural centres by teleoncology models. It is important to note, however, that these models require appropriate governance, and that adequate health care system resources be directed to rural centres.

Within the TTN, the quality of care provided through teleoncology is closely related to the adequacy of the rural workforce and strict governance of chemotherapy management.15 Workforce requirements and governance of chemotherapy administration are the same for all sites. Medical oncologists from the TCC provide their outpatient services regularly and on demand via video-conferencing, and are also able to review and make decisions for admitted inpatients.14 These are the same medical oncologists who provide face-to-face care in Townsville. TCC-based oncologists are supported by general physicians, nurses, allied health professionals and pharmacists in the rural centres. These multidisciplinary services undertake initial consultations, monitoring and management of toxicity in ambulatory care, and inpatient settings and follow-up until the completion of a treatment program or referral to palliative services for end-of-life care. As part of this network and throughout our study, the Mount Isa Hospital was adequately resourced to provide services locally through a teleoncology model of care.15 As the scope of practice broadened and the complexity of cases increased over time, clinicians successfully lobbied for increased resources for Mount Isa to expand its rural service capabilities. The results of our study should therefore be applied with caution to centres with more limited resources.

Our study was designed to detect differences in toxicity profiles and dose intensities for treatment delivered by teleoncology (Mount Isa) or in person (Townsville). None were detected. However, further research is required with larger sample sizes to assess the statistical equivalence of these treatment modalities. Although our study was statistically powered for the analysis of differences in dose intensities associated with teleoncology and face-to-face models of care at the two hospitals, comparisons for individual tumour types would not be meaningful because of the small patient numbers for each tumour type. Selecting a matched patient sample at the TCC was considered, but it was difficult to compile a complete history of patient comorbidities because of the retrospective nature of the audit and the incomplete chart data. In reality, however, patients with severe comorbidities would not have received chemotherapy at either hospital, and lack of matching probably had only a minimal impact on the outcomes of our study.

As our data were not collected prospectively, it is possible that some adverse effects and other relevant data, including quality-of-life information, were not recorded and captured by the audit. However, serious adverse effects (NCI CTC grades 3 and 4) usually result in admission to hospital, and this would have been captured by admission records. In addition, any omission or delays in chemotherapy are likely to be documented in patient charts.

In conclusion, our results, together with those of telestroke studies and our earlier rural chemotherapy study,11 provide initial reassurance that high-quality and safe cancer care, including a variety of complex medical therapies, can be provided to rural patients closer to their homes by teleoncology and other telehealth models of care. By expanding the scope of practice and capabilities of rural health care systems through the use of telehealth models, rural patients may gain access to chemotherapy and other complex medical therapies similar to that of urban patients. To ensure a high level of safety and quality, centres embarking on providing chemotherapy and complex medical therapies in rural areas using telehealth models need to ensure that rural resources are adequate and that governance arrangements are strict.

Box 1 –
Demographic characteristics of the patients treated in Mount Isa and Townsville

	Mount Isa	Townsville

Number of patients	89	117
Sex
Male	43 (48%)	60 (51.3%)
Female	46 (52%)	57 (48.7%)
Ethnicity*
Indigenous	20 (22%)	7 (6.0%)
Non-Indigenous	69 (77%)	109 (94.0%)
Age, years (median, range)	58 (18–82)	59 (20–86)
Treatment intent
Curative/adjuvant	34 (38%)	56 (47.9%)
Palliative	55 (62%)	61 (52.1%)
Cancer type
Breast	24 (27%)	33 (28.2%)
Colon	10 (11%)	12 (10.3%)
Lung	21 (24%)	22 (18.8%)
Prostate	1 (1.1%)	2 (1.7%)
Rectal	7 (7.9%)	2 (1.7%)
Oesophagus/gastric	4 (4.5%)	2 (1.7%)
Neuroendocrine/gastrointestinal stromal tumour	1 (1.1%)	1 (0.9%)
Head/neck	0	2 (1.7%)
Other	21 (24%)	41 (35.0%)

*Indigenous v non-Indigenous: P < 0.001; there were no other statistically significant differences.

Box 2 –
Chemotherapy doses and toxicity rates, by site

	Mount Isa	Townsville

Number of patients	89	117
Cycles per line (mean ± SD)	5.38 ± 3.84	5.07 ± 4.80
Total number of cycles	626	799
Number of treatment lines (mean ± SD)	1.30 ± 0.65	1.36 ± 0.66
Rate of serious side effects (per patient)	4.4%	9.5%
Inpatient hospital admissions
Total number	30	50
Proportion of patients	28%	35.3%

P > 0.05 for all between-group comparisons.

Box 3 –
Chemotherapy doses and rates of side effects, by treatment intent and hospital

	Palliative (116 patients)		Curative/adjuvant (90 patients)
	Mount Isa	Townsville	Mount Isa	Townsville

Number of patients	55	61	34	56
Cycles per line (mean ± SD)	4.37 ± 2.41	4.47 ± 5.20	7.0 ± 5.02	5.70 ± 4.29
Number of lines (mean ± SD)	1.44 ± 0.76	1.45 ± 0.75	1.08 ± 0.29	1.27 ± 0.55
Total number of cycles	367	388	259	411
Rate of serious side effects (per patient)	5.4%	15%	2.9%	3.6%
Hospital admissions
Total number	24	33	6	17
Proportion of patients	36%	43%	15%	27%
Dose intensity, percentage* (mean ± SD)	97.4 ± 24.0	98.2 ± 16.1	84.4 ± 25.9	88.1 ± 25.9

* Actual dose, compared with planned dose. P > 0.05 for all between-group comparisons.

Box 4 –
Side effect profiles (National Cancer Institute Common Toxicity Criteria, grade 3 and 4 toxicity) for patients treated at Mount Isa and Townsville hospitals

	Overall (206 patients)		Palliative (116 patients)		Curative (90 patients)
	Mount Isa	Townsville	Mount Isa	Townsville	Mount Isa	Townsville

Neutropenia*	29%	18%	21%	23%	34%	13%
Nausea and vomiting	0	1.7%	0	0	0	3.3%
Diarrhoea	1.1%	6.9%	0	12%	1.8%	1.7%
Neuropathy	3.3%	1.7%	8.8%	0	0	3.3%
Fatigue	0	4.3%	0	1.8%	0	6.7%
Other	16%	26%	8.8%	21%	16%	30%

*More neutropenia was reported in Mount Isa, but this did not result in more hospital admissions.

Life expectancy discussions in a multisite sample of Australian medical oncology outpatients

Why provide life expectancy information? An integral part of patient-centred cancer care is ensuring that information, communication and education provided to patients meets their needs, preferences and values.1 Between 50% and 70% of patients with cancer want numerical estimates of their life expectancy.2–6 Assessing and responding to patient preferences about life expectancy information is therefore a necessary component of patient-centred care, and can assist patients in making informed and effective decisions about their care. Misperceptions of life expectancy by patients, however, can also influence aspects of care, such as decisions about continuing life-prolonging treatments that may diminish their quality of life.7,8

Discussing life expectancy is a complex task. Clinicians must 1) establish how much, and in what detail patients want to know; 2) offer timely information that facilitates decision making about treatment and informed consent; 3) provide information consistent with patient preferences; 4) communicate the limitations inherent to prognoses; 5) present information in formats that aid understanding; and 6) ensure that information is communicated sensitively.6,9 Self-reports by patients about their awareness and understanding of what they have been told about life expectancy arguably comprise the gold standard measure of quality in this area.10,11

Is there concordance between patient preferences and experiences of discussions of life expectancy? The proportion of patients with cancer who reportedly discuss life expectancy is variable. Using direct observation, one study found that 58% of incurable oncology patients were told about life expectancy.12 Lower rates of disclosure (27%–53%) have been reported in studies based on patient self-reports.6,13 While discrepancies between preferences and experiences regarding prognosis information have been reported,6,14 few studies have focused specifically on life expectancy information. One investigation found that 47% of patients who wanted life expectancy information did not receive it, and 4% had received information they did not want.5 The preferences of patients receiving radiation therapy for cancer in Australia regarding who should initiate life expectancy discussions (the patient or their doctor) and their actual experiences were not aligned in 40% of instances.13

Previous studies of this question have been limited by convenience samples or low response rates (24% in one study5), or by including only participants with a single cancer type from a single treatment centre.4 The degree to which these data can be generalised to all cancer patients is therefore questionable. The literature indicates that patients’ preferences and experiences of prognosis discussions may vary according to their age, sex, marital status, ethnic background, education, disease status, time since diagnosis, cancer type and psychological wellbeing.4–6,15,16 Given the emphasis on reducing disparities in health care,1 it is important to explore factors associated with misalignment of patient preferences and experiences, and to identify subgroups who are less likely to receive the desired information.

Our multisite study aimed to identify:

the proportion of patients who received their preferred level of life expectancy information; and
the sociodemographic, clinical and psychological factors associated with patients’ perceptions of receiving too little, too much, or the desired amount of life expectancy information.

Methods

Patient sample

Eligible medical oncology treatment centres were those providing care for at least 400 new cancer patients each year, and were nominated by state-based research representatives to reflect the relative distribution of public, private, metropolitan and regional hospitals across each of the six Australian states. Invitations were sent to the 51 nominated centres by representatives on behalf of the research team.

Eligible patients had a confirmed cancer diagnosis, were attending the clinic for their second or subsequent appointment (to ensure that patients had experienced cancer care at the centre before answering questions about this treatment), were at least 18 years old, and were able to read and understand English. They were judged by clinical staff to be physically and mentally able to give informed consent and to complete the survey.

Informed consent was obtained by a researcher or clinic staff member by consecutively approaching eligible patients while they waited for their outpatient appointments. To assess consent bias, those who withheld consent were asked to provide their age and sex (this was not done for consenters). Consenting patients were asked to complete a pen-and-paper survey, either in the clinic or at home. Non-responders were sent reminder letters 2–3 weeks and 5–6 weeks after recruitment.

The survey instrument

Development of the measure

Survey items (outcome and associate items) were distributed to a sample of consumer advocates for qualitative feedback on item comprehensibility and relevance. Items were then piloted with 324 patients, and then revised to improve their quality and acceptability. The revised survey (detailed below) was completed by the participants in our study.

Life expectancy item

Participants were asked “Which of the following best describes your experience of discussions with your cancer doctor(s) about how cancer may affect the length of your life (your life expectancy)?” A question stem and five response options were provided: “My cancer doctor(s) at this hospital has discussed or given me …”:

More information than I wanted about my life expectancy;
All the information that I wanted about my life expectancy;
Some of the information that I wanted about my life expectancy;
None of the information that I wanted about my life expectancy; or
No information about life expectancy, but I haven’t wanted information.

Associate variables

All associate variables were obtained from patient self-reports. Sociodemographic items included sex, age, marital status, education (a proxy for socioeconomic status17), and ethnic background (for participants born outside Australia or who identified as being Aboriginal and/or Torres Strait Islander).

Clinical items included the patient’s most recent cancer type, stage of cancer at diagnosis, current remission status, time since diagnosis, and main reason for visit to the clinic on the day of recruitment.

Psychological wellbeing was measured with the Hospital Anxiety and Depression Scale (HADS), a 14-item survey with two subscales, anxiety and depression. Each item is rated on a four-point scale; scores range from 0 to 21 for each subscale. A mean cut-off score of 8 on each subscale optimises its sensitivity and specificity as a screening instrument.18

Statistical analysis

Stata/IC 11.1 (StataCorp) was used for all analyses. Consent bias (age, sex) was assessed with χ² analyses. Frequency tables were calculated for each of the five life expectancy response options. A multinomial logistic regression, adjusted for clustering of results within centres by jacknife estimation, examined factors associated with the alignment of patient preference and experience. Key variables were selected a priori. Four preference–experience outcome categories were generated:

too much information (“More information than I wanted”);
too little information (“Some of the information I wanted” or “None of the information I wanted”);
no information wanted or received (“No information about life expectancy, but I haven’t wanted information”); and
received all the information wanted (“All the information that I wanted”).

The fourth category was the reference category. While the third and fourth categories both included patients whose preferences and experiences were aligned, the large sample size offered an opportunity to explore these groups separately. Odds ratios (ORs) with 95% confidence intervals and the results of adjusted Wald tests are reported.

Ethics approval

The University of Newcastle Human Research Ethics Committee and the ethics committees of the participating health services approved the study (ref. H-2010-1324).

Results

Of 51 medical oncology treatment centres that were approached, 14 consented to participate in the study (27% consent rate). Two consenting centres did not participate: one had an ethics process that was too expensive, and the other chose not to participate after consenting. Eleven of the participating treatment centres were public medical centres; nine were located in metropolitan and three in regional areas. At least one centre from each Australian state participated. Patients from 11 centres received the survey items on life expectancy during the recruitment period.

Patient sample

Of the 2167 eligible patients, 1431 returned a survey (Box 1). There were significant sex (χ²[1] = 12.3, P < 0.001; more women) and age differences (χ²[5] = 13.3, P = 0.02; more older patients) compared with those who withheld consent to participate. The age of those who initially consented but later did not respond could not be determined. Box 2 summarises the characteristics of the 1431 participants and the non-consenters.

Do patients receive their preferred level of information about life expectancy?

Of the 1431 responders, 1361 (95.1%) completed the life expectancy item of the survey. Responders did not differ from non-responders with respect to age (χ²[5] = 7.1, P = 0.21), sex (χ²[1] = 2.2, P = 0.14), treatment centre attended (χ²[10] = 13.8, P = 0.18), cancer type (χ²[4] = 4.4, P = 0.36) or remission status (χ²[2] = 0.2, P = 0.91), but participants who did not complete the item were less likely to have had advanced cancer at diagnosis (χ²[2] = 10.7, P = 0.005).

As summarised in Box 3, 72% of patients received the information that they desired; that is, 50% received all the information that they wanted, and 22% neither wanted nor received any information. A mismatch between preferences and experiences was reported by 388 patients (28%), of whom 24% reported not receiving enough information and 4% reported receiving too much.

After adjusting for clustering within treatment centres, no variation between institutions in the perception of life expectancy discussions was identified (post hoc χ²[10] = 35.9, P = 0.21).

What factors are associated with the alignment of preferences and experiences?

Box 4 and the Appendix present the results of our multinomial logistic regression. The reference group included those who received the desired amount of information about life expectancy. The odds of receiving too little information were greater for patients not in remission (OR, 1.8; 95% CI, 1.2–2.6), who did not know their cancer stage at diagnosis (OR, 3.6; 95% CI, 1.6–8.1), or who were likely to have anxiety (OR, 1.5; 95% CI, 1.0–2.1) or depression (OR, 1.5; 95% CI, 1.0–2.2). Younger patients (OR, 1.4; 95% CI, 1.0–2.0), those with a more progressed cancer (OR, 2.0; 95% CI, 1.0–4.0) or who did not know their stage at diagnosis (OR, 4.4; 95% CI, 1.7–11.8) were more likely to receive too much information. Older patients (OR, 1.1; 95% CI, 1.0–1.2) and those who did not know their stage at diagnosis (OR, 2.8; 95% CI, 1.6–5.0) were more likely to report that they neither wanted nor received information.

Discussion

Do patients receive their preferred level of information about life expectancy?

Almost three in four patients (72%) reported receiving the desired level of information about life expectancy: half of our sample (50%) reported receiving adequate levels of information, while 22% neither wanted nor received any information. This confirms previous findings that patients have different life expectancy information needs.2–5

The degree to which patients receive life expectancy information that aligns with their preferences has not improved dramatically.5 Discordant preference–experience outcomes most commonly involved patients receiving less information than they desired. Clinicians may avoid discussing prognosis and life expectancy in day-to-day practice because of uncertainty about the accuracy of estimates, a perceived lack of time, or concerns about their ability to deal with their patients’ emotions.6,19 Clinicians may wait for patients to request life expectancy information before providing it, or discuss it in ways that may be difficult for some patients to understand.5,6,19 Given that a minority of patients (4%) received more information about life expectancy than they wanted, and a substantial minority prefer to receive no information (22%), it is not appropriate to provide comprehensive information to all patients. Clinicians should take an individualised approach when providing life expectancy information.

Who gets too little information?

Patients not in remission were more likely to receive less life expectancy information than desired. As communicating a poor prognosis is complex, patients may not always understand such information in a manner that aids their decision making.11 As a consequence, clinicians should ensure they have the necessary skills to sensitively provide such information to patients who wish to be informed.20

Elevated anxiety and depression scores were associated with receiving too little information about life expectancy. Clinicians may withhold information from patients whom they perceive to be anxious or depressed, particularly if the prognosis is poor.10 On the other hand, receiving less information than desired may itself increase anxiety and depression.

Who gets too much information?

Patients who received too much life expectancy information were younger and reported having more advanced cancer at diagnosis. This finding may reflect assumptions by clinicians that younger patients will want life expectancy information. It may be especially important for those with a poor prognosis in order to guide decisions about treatment. Our findings may also indicate that clinicians assume that those with a poor prognosis want to receive this type of information.

Who neither wants nor receives information?

Consistent with the results of previous research, patients who neither wanted nor received life expectancy information were significantly more likely to be older.15,21,22 Older patients frequently report higher levels of satisfaction with cancer care.23 Younger patients are more likely to request and receive information about the prognosis,5,20 possibly reflecting increasing expectations of being involved in treatment decisions, and also the potential value of this information for assisting them regain some degree of control over their life plans.6

Stage of cancer at diagnosis unknown

One of the strongest and most consistent associate predictors was not knowing the stage of cancer at diagnosis (9% of sample). These patients had greater odds of reporting that they had received too little or too much information, or that they neither wanted nor received any information. This may reflect a general dissatisfaction with the providing of information, or that cancer diagnosis staging and prognosis was unknown to their doctors. Alternatively, it could reflect a generalised communication problem or the patient’s difficulty in understanding the clinical aspects of their diagnosis. Particular patient groups may need additional support to ensure their accurate understanding of clinical information.

Strengths and limitations of our study

While women were overrepresented and younger patients underrepresented in our sample, this multisite study is the largest and most representative to examine the research question. We are unable to determine whether cancer type profile and other clinical characteristics of the sample are representative of these parameters for the overall population of patients with cancer. While patient reports are the gold standard for assessing patient-centred care,1 they can be influenced by recall bias. Clinical characteristics were assessed by patient self-report, which may be less accurate than data obtained from medical or registry records. However, the clinical items in the survey were pilot-tested and structured to facilitate understanding by patients (lay terms, the use of examples) and to increase accuracy. The participants’ levels of health literacy, which is correlated with information-seeking behaviours and understanding provided information, were not measured, as the study aimed to assess the experiences and preferences of all patients who attended the treatment centres. The preferences of patients and their experiences may change over time as their circumstances change, so that future research should apply a longitudinal study design.

Clinical implications

To improve care delivery, health care teams should regularly collect patient feedback on the quality of care at both the patients’ and the institutional levels. Clinicians could ask patients whether they have received and understood the information about life expectancy provided to them. Enquiring about their information preferences should occur across several consultations to allow patients to process information and to formulate questions.9 At the institutional level, regular feedback about life expectancy information could be incorporated into patient experience surveys. As this study found, asking patients about this topic is practicable and acceptable, but current patient experience surveys do not routinely explore this aspect of care.

It might be expected that delivering information about life expectancy would be a matter of institutional policy. However, the lack of variation across centres suggests that these discussions are regulated by individual clinicians, rather than by policies or monitoring processes. To improve care for the 28% of patients whose preferences and experiences were not aligned, institution-wide policies and routine feedback should be considered.

Conclusions

Discussing life and death is emotional for patients, their families and their friends. That fact that 28% of cancer patients do not receive the level of information about life expectancy that they desire highlights the difficulties associated with discussing this sensitive topic. While not all patients want to receive detailed information, discordance was more often the result of patients wanting more rather than less information. The first step for clinicians should therefore be to ask whether the individual patient wants to know this information, in what format, and at what level of detail (eg, estimated life expectancy, cancer staging, prospects for cure, aim of cancer treatments). Australian consensus guidelines9 are available to assist clinicians in communicating information about life expectancy, including advice about using generic communication skills (eg, body language and active listening), and about clarifying the questions of patients and caregivers and addressing their information needs in an ongoing conversation over time. While the onus of responsibility remains with the clinician to ensure that life expectancy discussions occur in accordance with patient preferences, question prompt lists have been identified as helpful for enabling patients to obtain the information they desire.24

Box 1 –
Patient recruitment and data collection process

* Forty patients were ineligible for more than one reason.

Box 2 –
Sociodemographic and clinical characteristics of the study participants, and age and sex data for non-consenting patients^∗

	Study sample	Non-consenters

Total number	1431	401
Sex
Male	601 (42%)	200 (52%)
Female	828 (58%)	184 (48%)
Missing data	2	17
Age at diagnosis, years (mean ± SD)	62.5 ± 12.4
18–34 years	33 (2%)	16 (4%)
35–44 years	88 (6%)	17 (4%)
45–54 years	242 (18%)	57 (15%)
55–64 years	395 (29%)	120 (31%)
65–74 years	413 (30%)	100 (26%)
≥ 75 years	206 (15%)	76 (20%)
Missing data	54	15
Marital status
Married or in a relationship	906 (65%)
Single, divorced or widowed	489 (35%)
Missing data	36
Education
Primary school	97 (7%)
High school	600 (43%)
Trade or university	637 (46%)
Other	48 (3%)
Missing data	49
Minority group
Aboriginal and/or Torres Strait Islander	19 (1%)
Not born in Australia	438 (31%)
Neither	935 (67%)
Missing data	39
Cancer type
Breast	454 (33%)
Colorectal	236 (17%)
Lung	140 (10%)
Upper gastrointestinal	130 (9%)
Prostate	78 (6%)
Other urogenital	75 (5%)
Haematological	60 (4%)
Gynaecological	49 (4%)
Other	154 (11%)
Missing data	55
Stage of cancer at diagnosis
Early	818 (61%)
Advanced	408 (30%)
Don’t know	117 (9%)
Missing data	88
Current remission status
In remission	409 (30%)
Not in remission	559 (41%)
Don’t know	411 (30%)
Missing data	52
Months since cancer diagnosis
Less than 6 months	425 (30%)
6–12 months	260 (19%)
13–24 months	244 (17%)
More than 24 months	473 (34%)
Missing data	29
Reason for clinic visit
To discuss treatment options	117 (9%)
To receive treatment or check-up during treatment	801 (61%)
Post-treatment follow-up	405 (31%)
Missing data	108
Treatment received to date^†
Surgery	977 (70%)
Chemotherapy	113 (81%)
Radiotherapy	664 (51%)
Hormonal manipulation	312 (24%)
Biological therapies	146 (11%)
Treatment centre
A 136 (10%) G	105 (7%)
B 111 (8%) H	155 (11%)
C 159 (11%) I	163 (11%)
D 101 (7%) J	140 (10%)
E 86 (6%) K	158 (11%)
F 117 (8%)

∗ Percentages in this table exclude missing data. † Patients may have received more than one treatment type.

Box 3 –
Concordance between patient preferences and experiences in discussions of life expectancy^∗

Exploring the value of interprofessional student-led clinics for chronic disease patients

Interprofessional student-led clinics have recently been established to extend educational capacity beyond traditional single-discipline placements in the acute sector and to facilitate the development of a collaborative approach to health care.1 Although student satisfaction with interprofessional education (IPE) is common,2 it is unclear whether IPE improves chronic disease management, a global priority of health care practice.3 This study explored the impact of interprofessional student-led clinics on chronic disease management in the primary care setting.

Patients (n = 44) with chronic disease at a primary care practice in Melbourne were invited to attend a student-led interprofessional clinic made up of senior university student volunteers from medicine, nursing, occupational therapy, pharmacy and physiotherapy. In a one-hour consultation between May and September 2014, mixed-discipline student teams interviewed patients to explore their perceived health issues, review medications and make recommendations to the treating general practitioner. Postconsultation, health issues identified by students were analysed to determine if any new health concerns had been identified. At 6-week follow-up, patient files were audited and GPs consulted to determine whether the students’ recommendations had been implemented.

New health issues were identified in five patients. Medication modifications were suggested for 17 patients, of which action was undertaken for 12 patients at follow-up. Referrals for additional services or support were recommended for 29 patients, of which action was undertaken for nine patients at follow-up. Referral recommendations included physiotherapy, podiatry and diabetes education, and new preventive health approaches were commonly adopted.

The ability of student teams to identify previously unknown health issues and propose useful health management changes highlights the potential of IPE to improve the management of patients with chronic disease. Findings from this study resonate with previous literature which suggests IPE can lead to improved patient health management.4 Over time, GPs acquire a breadth of knowledge about their patients’ health, but student teams in this study only had a single consultation with each patient. Longitudinal exposure to interprofessional consultations may facilitate students to evaluate and further refine their health management recommendations.

This study builds upon the literature examining chronic disease management by interprofessional student teams in primary care. The use of outcome measures such as health issues, medication and referral recommendations facilitated an objective assessment of health management changes. Limitations of this study are that all participants were volunteers and a comparison group was not employed. There is also the potential for experimenter bias, as one of the authors (M D) was a treating GP for some of the patients in the study. Employing a random sample of patients in future studies is recommended.

Positive early findings suggest further investigation of the potential of interprofessional student-led clinics to improve health care management for patients with chronic disease is warranted.