A Markov chain model for analysis of physician workflow in primary care clinics.

This paper studies physician workflow management in primary care clinics using terminating Markov chain models. The physician workload is characterized by face-to-face encounters with patients and documentation of electronic health record (EHR) data. Three workflow management policies are considered: preemptive priority (stop ongoing documentation tasks if a new patient arrives); non-preemptive priority (finish ongoing documentation even if a new patient arrives); and batch documentation (start and finish documentation when the desired number of tasks is reached). Analytical formulas are derived to quantify the performance measures of three management policies, such as physician's daily working time, patient's waiting time, and documentation waiting time. A comparison of the results under three policies is carried out. Finally, a case study in a primary care clinic is carried out to illustrate model applicability. Such a work provides a quantitative tool for primary care physicians to design and manage their workflow to improve care quality.

Reducing COPD readmissions through predictive modeling and incentive-based interventions.

This paper introduces a case study at a community hospital to develop a predictive model to quantify readmission risks for patients with chronic obstructive pulmonary disease (COPD), and use it to support decision making for appropriate incentive-based interventions. Data collected from the community hospital's database are analyzed to identify risk factors and a logistic regression model is developed to predict the readmission risk within 30 days post-discharge of an individual COPD patient. By targeting on the high-risk patients, we investigate the implementability of the incentive policy which encourages patients to take interventions and helps them to overcome the compliance barrier. Specifically, the conditions and scenarios are identified for either achieving the desired readmission rate while minimizing the total cost, or reaching the lowest readmission rate under incentive budget constraint. Currently, such models are under consideration for a pilot study at the community hospital.

The impact of e-visits on patient access to primary care.

To improve patient access to primary care, many healthcare organizations have introduced electronic visits (e-visits) to provide patient-physician communication through secure messages. However, it remains unclear how e-visit affects physicians' operations on a daily basis and whether it would increase physicians' panel size. In this study, we consider a primary care physician who has a steady patient panel and manages patients' office and e-visits, as well as other indirect care tasks. We use queueing-based performance outcomes to evaluate the performance of care delivery. The results suggest that improved operational efficiency is achieved only when the service time of e-visits is smaller enough to compensate the effectiveness loss due to online communications. A simple approximation formula of the relationship between e-visit service time and e-visit to office visit referral ratio is provided serving as a guideline for evaluating the performance of e-visit implementation. Furthermore, based on the analysis of the impact of e-visits on physician's capacity, we conclude that it is not the more e-visits the better, and the condition for maximal panel size is investigated. Finally, the expected outcomes of implementing e-visits at Dean East Clinic are discussed.

Classification of Opioid Usage Through Semi-Supervised Learning for Total Joint Replacement Patients.

Opioid misuse and overdose have become a public health hazard and caused drug addiction and death in the United States due to rapid increase in prescribed and non-prescribed opioid usage. The misuse and overdose are highly related to opioid over-prescription for chronic and acute pain treatment, where a one-size-fits-all prescription plan is often adopted but can lead to substantial leftovers for patients who only consume a few. To reduce over-prescription and opioid overdose, each patient's opioid usage pattern should be taken into account. As opioids are often prescribed for patients after total joint replacement surgeries, this study introduces a machine learning model to predict each patient's opioid usage level in the first 2 weeks after discharge. Specifically, the electronic health records, patient prescription history, and consumption survey data are collected to investigate the level of short-term opioid usage after joint replacement surgeries. However, there are a considerable number of answers missing in the surveys, which degrades data quality. To overcome this difficulty, a semi-supervised learning model that assigns pseudo labels via Bayesian regression is proposed. Using this model, the missing survey answers of opioids amount taken by the patients are predicted first. Then, based on the prediction, pseudo labels are assigned to those patients to improve classification performance. Extensive experiments indicate that such a semi-supervised learning model has shown a better performance in the resulting patients classification. It is expected that by using such a model the providers can adjust the amount of prescribed opioids to meet each patient's actual need, which can benefit the management of opioid prescription and pain intervention.

A practice guide for continuous opioid therapy for refractory daily headache: patient selection, physician requirements, and treatment monitoring.

OBJECTIVES: To provide a guide to the use and limitations of continuous opioid therapy (COT, or daily scheduled opioids) for refractory daily headache, based on the best available evidence and expert clinical experience. BACKGROUND: There has been a dramatic increase in opioid administration over the past 25 years, with limited evidence of efficacy for either pain reduction or increased function, and increasing evidence of adverse effects, including headache chronification. To date, there has been no consensus on headache-specific guidelines for selecting patients for COT, physician requirements, and treatment monitoring. METHODS: A multidisciplinary committee of physicians and allied health professionals with extensive experience and expertise in the administration of opioids to headache patients, undertook a review of the available evidence from the research and clinical literature (using the PubMed database for articles through December 2009) to develop headache-specific treatment recommendations. This guide reflects the opinions of its authors and is not an official document of the American Headache Society. RESULTS: The guide identifies factors that would qualify or disqualify the use of COT, including, determination of intractability prior to initiating COT, requisite experience of the prescriber, and requirements for a formal monitoring system to assess appropriate use, safety, efficacy, and functional impact. An appendix reviews the available evidence for efficacy of COT in chronic headache and noncancer pain, paradoxical effects (opioid-induced hyperalgesia, medication overuse headache, opioid-related reduction in triptan and nonsteroidal anti-inflammatory drug efficacy), other adverse effects (nausea and constipation, insomnia and sleep apnea, respiratory depression and sudden cardiac death, reductions in sex hormones, issues during pregnancy, neurocognitive functioning), and issues related to comorbid psychiatric disorders. CONCLUSIONS: Only a select and very limited group (estimate of 10-20%) of refractory headache patients who meet criteria for COT respond with convincing headache reduction and functional improvement over the long-term. Conservative and empirically based guidelines will help identify those patients for whom a COT trial may be appropriate, while protecting their welfare and safety.

An Analytical Framework for TJR Readmission Prediction and Cost-Effective Intervention.

This paper introduces an analytical framework for assessing the cost-effectiveness of intervention strategies to reduce total joint replacement (TJR) readmissions. In such a framework, a machine learning-based readmission risk prediction model is developed to predict an individual TJR patient's risk of hospital readmission within 90 days post-discharge. Specifically, through data sampling and boosting techniques, we overcome the class imbalance problem by iteratively building an ensemble of models. Then, utilizing the results of the predictive model, and by taking into account the imbalanced misclassification costs between readmitted and nonreadmitted patients, a cost analysis framework is introduced to support decision making in selecting cost-effective intervention policies. Finally, using this framework, a case study at a community hospital is presented to demonstrate the applicability of the analysis.

Protecting healing relationships in the age of electronic health records: report from an international conference.

We present findings of an international conference of diverse participants exploring the influence of electronic health records (EHRs) on the patient-practitioner relationship. Attendees united around a belief in the primacy of this relationship and the importance of undistracted attention. They explored administrative, regulatory, and financial requirements that have guided United States (US) EHR design and challenged patient-care documentation, usability, user satisfaction, interconnectivity, and data sharing. The United States experience was contrasted with those of other nations, many of which have prioritized patient-care documentation rather than billing requirements and experienced high user satisfaction. Conference participants examined educational methods to teach diverse learners effective patient-centered EHR use, including alternative models of care delivery and documentation, and explored novel ways to involve patients as healthcare partners like health-data uploading, chart co-creation, shared practitioner notes, applications, and telehealth. Future best practices must preserve human relationships, while building an effective patient-practitioner (or team)-EHR triad.

In-office discussions of migraine: results from the American Migraine Communication Study.

BACKGROUND: Research indicates that successful migraine assessment and treatment depends on information obtained during patient and healthcare professional (HCP) discussions. However, no studies outline how migraine is actually discussed during clinical encounters. OBJECTIVE: Record naturally occurring HCP-migraineur interactions, analyzing frequency and impairment assessment, and preventive treatment discussions. DESIGN: HCPs seeing high volumes of migraineurs were recruited for a communication study. Patients likely to discuss migraine were recruited immediately before their normally scheduled appointment and, once consented, were audio- and video-recorded without a researcher present. Separate post-visit interviews were conducted with patients and HCPs. All interactions were transcribed. PARTICIPANTS: Sixty patients (83% female; mean age 41.7) were analyzed. Patients were diagnosed with migraine 14 years and experienced 5 per month, on average. APPROACH: Transcripts were analyzed using sociolinguistic techniques such as number and type of questions asked and post-visit alignment on migraine frequency and impairment. American Migraine Prevalence and Prevention Study guidelines were utilized. RESULTS: Ninety-one percent of HCP-initiated, migraine-specific questions were closed-ended/short answer; assessments focused on frequency and did not focus on attention on impairment. Open-ended questions in patient post-visit interviews yielded robust impairment-related information. Post-visit, 55% of HCP-patient pairs were misaligned regarding frequency; 51% on impairment. Of the 20 (33%) patients who were preventive medication candidates, 80% did not receive it and 50% of their visits lacked discussion of prevention. CONCLUSIONS: Sociolinguistic analysis revealed that HCPs often used narrowly focused, closed-ended questions and were often unaware of how migraine affected patients' lives as a result. It is recommended that HCPs assess impairment using open-ended questions in combination with the ask-tell-ask technique.

Collaboration saves time.

Caring for patients today is very complicated and involves many clinical and administrative tasks. Clinicians are often asked to fill out a wide variety of forms, including forms that verify that the patient's clinical status is stable. Currently, these forms are filled out manually by the clinician or staff. Clinicians use electronic medical records (EMRs) have the potential for significant time savings if the EMR can be used to eliminate manually loading data already housed in the EMR. This article describes how collaboration between a government agency and a medical group that uses the Epic EMR resulted in an electronic version of a commonly used form. Once implemented, this form resulted in a significant time savings for the clinician. It is hoped that this project will serve as a template for future similar projects that could result in more efficient use of clinician and office staff's time.