An Exploratory Case Study to Understand Primary Care Users and Their Data Quality Tradeoffs

Data quality in primary care, Scotland

BACKGROUND AND AIMS

This project explores primary care data quality (DQ) across Scotland.

METHODS AND RESULTS

A survey was sent to primary care staff in winter 2019. National data regarding Quality and Outcomes Framework (QOF) performance indicators and the GP software system used was obtained, analysed with T-tests and Chi-squared tests. Overall QOF performance with non-financial incentives from 918 practices was 77%. There was no significant difference with overall QOF performance against GP system (p = 0.46) or if the practice had a coder (p = 0.06). From the survey, search systems that make it hard to search for particular codes was the most important barrier to DQ; 61% of respondents (n = 491) felt there was particular information GPs should code, 16% of respondents stated that hospital discharge letters generally include corresponding codes and 9% for outpatient correspondence; 43% stated their practice had undertaken steps to improve DQ, training was the most common initiative, followed by workflow optimisation, dedicated coder(s), audit, guidelines and using code lists; 80% (n = 475) of respondents had received training in using their GP system, an average of eight years ago.

CONCLUSION

Obtaining improved GP systems, training, agreeing what GPs should code and improving transfer of data should be explored.

Applying Persuasive Design Techniques to Influence Data-Entry Behaviors in Primary Care: Repeated Measures Evaluation Using Statistical Process Control

Background

Persuasive design is an approach that seeks to change the behaviors of users. In primary care, clinician behaviors and attitudes are important precursors to structured data entry, and there is an impact on overall data quality. We hypothesized that persuasive design changes data-entry behaviors in clinicians and thus improves data quality.

Objective

The objective of this study was to use persuasive design principles to change clinician data-entry behaviors in a primary care environment and to increase data quality of data held in a family health team’s reporting system.

Methods

We used the persuasive systems design framework to describe the persuasion context. Afterward, we designed and implemented new features into a summary screen that leveraged several persuasive design principles. We tested the influence of the new features by measuring its impact on 3 data quality measures (same-day entry, record completeness, and data validity). We also measured the impacts of the new features with a paired pre-post t test and generated XmR charts to contextualize the results. Survey responses were also collected from users.

Results

A total of 53 users used the updated system that incorporated the new features over the course of 8 weeks. Based on a pre-post analysis, the new summary screen successfully encouraged users to enter more of their data on the same day as their encounter. On average, the percentage of same-day entries rose by 10.3% for each user (P<.001). During the first month of the postimplementation period, users compensated by sacrificing aspects of data completeness before returning to normal in the second month. Improvements to record validity were marginal over the study period (P=.05). Statistical process control techniques allowed us to study the XmR charts to contextualize our results and understand trends throughout the study period.

Conclusions

By conducting a detailed systems analysis and introducing new persuasive design elements into a data-entry system, we demonstrated that it was possible to change data-entry behavior and influence data quality in a reporting system. The results show that using persuasive design concepts may be effective in influencing data-entry behaviors in clinicians. There may be opportunities to continue improving this approach, and further work is required to perfect and test additional designs. Persuasive design is a viable approach to encourage clinician user change and could support better data capture in the field of medical informatics.

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification

INTRODUCTION

An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of the acute coronary syndrome (ACS) was developed and used in an experimental study.

METHODS

A combination of data, text, process mining techniques, and machine learning approaches for the analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for the simulation of patient flow was proposed. The performed analysis of EHRs for ACS patients enabled identification of several classes of clinical pathways (CPs) which were used to implement a more realistic simulation of the patient flow. The developed solution was implemented using Python libraries (SimPy, SciPy, and others).

RESULTS

The proposed approach enables more a realistic and detailed simulation of the patient flow within a group of related departments. An experimental study shows an improved simulation of patient length of stay for ACS patient flow obtained from EHRs in Almazov National Medical Research Centre in Saint Petersburg, Russia.

CONCLUSION

The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for the implementation of a simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others.