Deep Claim: Payer Response Prediction from Claims Data with Deep Learning (2007.06229v1)

Abstract: Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood of payment) from claims data with a high degree of accuracy and precision is anticipated to improve healthcare staffs' performance productivity and drive better patient financial experience and satisfaction in the revenue cycle (Barkholz, 2017). However, constructing advanced predictive analytics models has been considered challenging in the last twenty years. That said, we propose a (low-level) context-dependent compact representation of patients' historical claim records by effectively learning complicated dependencies in the (high-level) claim inputs. Built on this new latent representation, we demonstrate that a deep learning-based framework, Deep Claim, can accurately predict various responses from multiple payers using 2,905,026 de-identified claims data from two US health systems. Deep Claim's improvements over carefully chosen baselines in predicting claim denials are most pronounced as 22.21% relative recall gain (at 95% precision) on Health System A, which implies Deep Claim can find 22.21% more denials than the best baseline system.

• The paper proposes Deep Claim, a multi-task deep learning framework utilizing a context-dependent latent space to predict healthcare payer responses from raw claims data without extensive feature engineering.
• Evaluated on real-world datasets, Deep Claim achieved a 22.21% relative recall gain in denial prediction and a 23.9% reduction in MAE for response date prediction compared to baselines.
• A key feature of Deep Claim is its ability to identify questionable claim fields via gradient analysis, enhancing interpretability and aiding in identifying potential denial issues.

The paper "Deep Claim: Payer Response Prediction from Claims Data with Deep Learning" addresses the challenge of predicting payer responses in health care claims using deep learning methodologies. Health care systems face significant financial burdens due to claim denials and the high administrative costs of recovering these denials. This paper proposes a novel framework named Deep Claim that aims to enhance the accuracy of predicting claim denials and responses, thereby optimizing the revenue cycle management process in health care institutions.

Key Contributions:

1. Framework Architecture:
   • Deep Claim employs a multi-task deep learning model that represents patients' historical claim records through a context-dependent compact latent space. This approach captures the complex dependencies inherent in the high-dimensional claims data. The model uses a gating mechanism and bilinear models to process raw claims data efficiently without relying on extensive feature engineering or domain expertise.
2. Evaluation and Results:
   • The framework's efficacy was validated using a large dataset, consisting of 2,905,026 de-identified claims from two different US health systems. The model significantly outperformed traditional baselines, achieving a 22.21% relative recall gain at 95% precision on Health System A. This indicates a marked improvement in detecting claim denials over existing systems.
   • In terms of payer response date prediction, Deep Claim reduced the Mean Absolute Error (MAE) by 23.9% compared to a simple average baseline for Health System A, indicating more accurate predictions of when insurers will respond.
3. Interpretability:
   • A salient feature of Deep Claim is its ability to identify questionable fields within a claim that may lead to denials, enhancing the interpretability of predictions. This is achieved by calculating the normalized gradient magnitude of the prediction outcome relative to each input feature, providing an insight into the areas that require attention before claim submission.

Technical Details:

• Claims Input Representation: Raw claim data is translated into a high-dimensional sparse vector comprising various features such as demographic information, diagnoses, and billed amounts. The system employs sub-context vectors for encoding procedures and diagnoses, using tokenization and normalization techniques.
• Claims Embedding Network: The embedding network leverages innovative approaches like gating mechanisms alongside bilinear pooling layers to transform the sparse high-dimensional input into a cohesive and informative lower-dimensional latent space representation.
• Multi-Task Learning Network: Deep Claim divides the prediction tasks into different categories, such as claim denial, denial reason codes (at both claim and service levels), and response date estimation. These tasks are solved concurrently to optimize the claim representation.

Discussion:

The authors highlight how Deep Claim, through its advanced predictive model, reduces the manual workload of health care staff and improves the claim approval process's financial efficiency. However, they acknowledge further work is needed to quantify Deep Claim's broader financial and administrative impact on health care systems. This includes assessing how this predictive tool can align with efforts to lessen patients' financial burdens and enhance care quality.

The framework represents a significant advancement in automating claim processing within health care systems but also points towards the necessity of integrating safeguards to prevent misuse for fraudulent activities. As the field progresses, the authors anticipate this deep learning approach will drive further automation and efficiency across the healthcare revenue cycle management spectrum.