The National Mortality Dataset (NMD) is an epidemiological dataset that contains annual death registrations for a given reference period. The NMD includes demographic variables including age, sex and country of birth as well as cause of death information coded to the International Classification of Diseases, 10th revision (ICD-10). Cause of death data includes both the underlying cause of death (UCoD) and associated causes of death (ACoD). The UCoD is the disease, condition or external event that initiated the train of morbid events leading to death. The ACoDs refer to all other conditions listed on the medical certificate of cause of death by the certifier. ACoDs can include diseases that are part of the chain of events leading to death, risk factors and co-morbid chronic conditions (Australian Bureau of Statistics 2020a). Understanding what ACoDs contributed to an individual’s death can provide insight into intervention points to prevent or decrease some causes of death. For example, modifiable risk factors such as smoking or hypertension can be targeted via public health campaigns, changing laws (ie. changing smoking regulations) and treatments (ie. diuretics or ACE inhibitors to manage hypertension) (Pilibosian, Wu, Aldrich and Wheeler 1999). Mental health conditions such as depression or drug and alcohol abuse are known to have a negative impact on health and are focus areas in national suicide prevention strategies (Lee and Jung 2006). Additionally knowing what drugs were present in an overdose can lead to reviews of drug prescription and use patterns (Department of Health 2017).
History will recall 2020 as a year that brought about new challenges and massive overhauls to the very way we work, teach, socialise, and interact. As COVID-19 lockdowns called for staying home, they also demanded new approaches to working, teaching, and learning. Speak to any Health Information Manager (HIM) and they will likely tell you the way they work has been transformed.
Clinical documentation in medical records can be broadly defined as any notation made by medical practitioners and other health professionals relating to a patient’s symptoms, past history, test results or treatments provided during a clinical encounter.Clinical documentation is critically linked to accurate clinical coding and accurate generation of the diagnosis related group (DRG) resulting in appropriate remuneration to the hospital in a case-mix based funding model (Cheng et al. 2009). With the exception of DRGs ending in ‘A’ or ‘Z’, a proportionately higher level of funding may be generated for complications and comorbidities that have been treated, investigated or have required increased clinical care but have not been coded within an admitted patient episode. These complications/comorbidities have the potential to change the Episode Clinical Complexity Score (ECCS) (previously known as Patient Clinical Complexity Level (PCCL)) which influences the acuity of the DRG and resulting reimbursement. The ECCS is a measure of the cumulative effect of a patient’s complications and comorbidities and is calculated for each episode of care using the Diagnosis Complexity Level (DCL) value assigned to each diagnosis code (including principal diagnosis) as a complexity weight.Simple checklists or proformas have been shown to improve the accuracy of the principal diagnosis and procedure code resulting in a higher remuneration for the organisation (Clement et al. 2013; Naran et al. 2014; Murphy et al. 2017). Such checklists are completed by the medical team responsible for the patient in hospital. This task is usually delegated to the most junior member of the team. The clinical coding department at the Mater Health Services have developed such checklists for various specialties. The uptake of the use of these checklists during the inpatient stay is variable.This study aims to confirm the increased remuneration achieved by using checklists and to compare the remuneration generated by a junior medical person as compared to a senior doctor using these lists to help with chart reviews.
Back in the late 1980s I began my health information management career in the public aged care sector. After several years working in various health information management, information technology and project management roles in acute health, I have recently returned to the not-for-profit aged care sector at an exciting time, where the skills of a Health Information Manager (HIM) can contribute to supporting safe and effective care as technology develops into a key component of clinical practice.
Residential aged care has recently been in the spotlight in Australia for all the wrong reasons. The Royal Commission into Aged Care Quality and Safety has highlighted many deficiencies in the provision of care (Commonwealth of Australia 2019), and the tragic impact of the COVID-19 pandemic has further laid bare the extent of the failings within the sector. Principal among the contributing factors identified has been insufficient and ineffective funding allocation along with inadequate staffing arrangements.