{'fraction_non_alphanumeric': 0.021617293835068056, 'fraction_numerical': 0.0032025620496397116, 'mean_word_length': 5.906077348066298, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Commentary: improving risk-adjustment models for capitation payment and global budgeting.

In this issue, Dr. Leida Lamers has contributed an excellent, well-written, understandable article that adds significantly to our understanding of risk adjustment in capitation payment systems (in this case the Netherlands sickness fund system) (Lamers 1998). The Lamers model has high applicability to strategic planning for healthcare resource allocation systems in many public and private healthcare systems around the world. The study clearly demonstrates the value of adding more health status information to improve the overall accuracy and goodness-of-fit of annual per capita expense prediction models. The author's results reinforce the independent contributions of demographics, diagnoses, and functional health status (FHS) to predicting annual per capita expenditures. In particular, the addition of information on hospital diagnoses during the previous year improves prediction over demographics only; adding information on hospital diagnoses over the previous three years improves prediction over hospital diagnoses for the past year only; and adding FHS information improves prediction over demographics and three years of inpatient diagnoses.