To compare the 2008 data from two initial lung cancer mapping reports, with the audit results from a new data set for patients diagnosed during 2010, and to identify any changes in timeliness across the patient pathway in South Island.

Additional information:

Have_(encrypted)_NHI Yes
Personally identifiable (e.g. linked to NHI numbers) and longitudinal or aggregated (e.g. for planning, clinical research etc.)? NHI
Volume of data (e.g. how many records) Since when? 2008: 223 pt; 2010: 428 pt
Scope Regional
Does the data contain diagnoses and clinical outcomes? Does the data contain procedures, device information and medication for therapy? Does this data set have cost / price data? Yes - Dx
Presence of Data dictionary? Column headings in Excel or any kind of data model if residing in a relational database (e.g. Access, SQL Server, Oracle etc.) NHI, DHB of domicile, date of diagnosis, date of birth, date of death, prioritised ethnicity, age at diagnosis, gender, smoking status; dates of referrals, FSA, treatment, and of MDM.
Indication of data quality (e.g. missing values, duplications, inconsistencies etc.). Q: Audits? How do you ensure the data is valid and correct? Numerous data cleansing and data quality checks were performed by a Central Region Technical Advisory Services (TAS) analyst on the data after the initial audit process. All date fields were systematically cleansed to ensure that all entries were valid dates, if more than one date was present, the earliest was used for analysis (unless notes for that patient indicated otherwise), with the other dates entered as comments. All text fields were cleansed for consistency, for example data entries for referral source for Ed/Acute patients were initially recorded as one of the following; “Acute”, “acute/Ed”, “ED”, “ED Acute” or “ED/Acute”. All were standardised to the latter, with a similar process applied to all text fields (excluding notes/comments). Any ambiguities in the data were referred back to the auditors to check. Patients to be removed from the analysis for reasons which included very limited patient records, patient treated privately, and patients found on investigation to not have lung cancer were identified. Altogether 15 patients were excluded from the final analysis. Other data quality checks included follow up for patients with an unknown or unclear inpatient/outpatient status, standardising the way that secondary and tertiary care FSAs had been categorised for Canterbury and Otago patients, revising the FSA department for patients first seen in emergency departments, and follow up of all patients with a waiting time that was either negative or excessively long. As a result of this process, several dates were amended, sometimes accompanied by changes to other data fields. For some patients with a negative or excess waiting time, the dates in the data set were correct, and an explanatory comment was added where appropriate.
Brief info about the systems and processes used to collect/manage data. Q: Where the data is collected, in what form, and accessibility? In 2008, patients for the Upper South Island were identified from a treatment database at Canterbury DHB. The resulting dataset is a subset of patients from the Upper South Island who were diagnosed with cancer in 2008. 2008 patients for the Southern (Southern) were identified from the cancer registry. There were a total of 223 patients included in the 2008 audit. In 2010, patient level data was sourced from the New Zealand Cancer Registry (NZCR) for patients domiciled in the South Island with a confirmed diagnosis of lung cancer (ICD 10 C33 to C34) in 2010. Amongst the data fields requested were unencrypted NHI (for patient identification), DHB of domicile, date of diagnosis, date of birth, date of death, prioritised ethnicity, age at diagnosis, gender, and smoking status. Additional data: referrals, FSA, treatment. MDM databases from Canterbury and Southern were obtained pre audit, and where a match with the NZCR data occurred, some additional fields in the spreadsheet were prepopulated. These fields included date of MDM and some referral and treatment dates. The actual audit process was conducted by two Southern Cancer Network project managers and involved the manual collection of lung cancer data sourced from patient's electronic files. Where there was missing electronic data, data was sourced from the patient's clinical notes.
Data format, e.g., data structure, data types, and storage form (relational database, Excel, csv, etc.). Excel spreadsheet