Clinician-directed improvement in the accuracy of hospital clinical coding.

'Payment by results' (PbR) remuneration for healthcare services relies on the accurate conversion of diagnoses into Healthcare Resource Group (HRG) codes that are then reimbursed. Inconsistencies in documentation can result in inaccuracies in this process, with consequent implications for measuring activity, disease incidence and organisational performance. The aim of this study was to determine if clinician involvement increases accuracy in the coding of medical cases. Selected records of medical patients admitted to a London NHS trust between November and December 2016 were reviewed by a coding auditor and a clinician. Any changes to the codes and HRG tariff were noted. In total, 123 cases were considered. Changes in code were made on 68 instances, resulting in an overall increase in remuneration of £39,215; an average of £318 per patient. The primary HRG code was changed in 31 cases which accounted for £28,040 of the increase in tariff. In conclusion, clinician involvement can help with documentation ambiguities, thus improving the accuracy of the coding process in a medical setting. Although such collaborative working offers advantages for both the clinician and the coding team, further work is required to investigate the feasibility of this recommendation on a larger scale.

Beneath the tip of the iceberg: using electronic referrals to map the unquantified burden of clinical activity in a urology service.

INTRODUCTION: The aim of this study was to inform health policy by demonstrating the benefits of an electronic referral (e-referral) system to study inpatient referrals to urology departments. METHODS: London North West Healthcare NHS Trust has over 800 beds and several secondary and tertiary care services. In January 2014, we designed an e-referral form to requisition urology input for in-patients using the Integrated Clinical Environment™ (ICE) platform. We retrospectively reviewed e-referral records from February to December 2014 for patient demography, the referring service and disease groups triggering a referral. RESULTS: There were 1192 referrals. There was a median of 107 e-referrals per month (IQR 97-123, range 91-132) and 4 per day (IQR 2-6, range 0-12). Weekend e-referrals were 127 (median 1, IQR 0-2, range 0-5) with 1065 e-referrals on weekdays (median 4, IQR 3-6, range 0-12). A total of 848 (71.1 %) patients were male, and 344 (28.9 %) were female. Patients' mean age was 63 (SD 21) years. Almost half (550, 46 %) were acute referrals. Amongst the acute referrals, the majority (466, 84.7 %) arose from A&E. From the non-acute conditions, 381 (59.3 %) patients were from the medical specialities and 246 (38.3 %) from surgical disciplines. The three largest disease categories were urolithiasis (287, 24.1 %), haematuria (185, 15.5 %) and for ex-catheterisation (102, 8.6 %). A qualitative review showed the strengths and weaknesses of the system, enabling improvements in operational efficiency. CONCLUSION: An e-referral system monitors activity accurately. Systemic improvement in referral pathways would lead to better patient care and enable services to factor in the unseen component of workload and prompt realistic staffing.