To Scan or Not to Scan: Development of a Clinical Decision Support Tool to Determine if Imaging Would Aid in the Diagnosis of Appendicitis.

BACKGROUND: Appendicitis is one of the most common surgically treated diseases in the world. CT scans are often over-utilized and ordered before a surgeon has evaluated the patient. Our aim was to develop a tool using machine learning (ML) algorithms that would help determine if there would be benefit in obtaining a CT scan prior to surgeon consultation. METHODS: Retrospective chart review of 100 randomly selected cases who underwent appendectomy and 100 randomly selected controls was completed. Variables included components of the patient's history, laboratory values, CT readings, and pathology. Pathology was used as the gold standard for appendicitis diagnosis. All variables were then used to build the ML algorithms. Random Forest (RF), Support Vector Machine (SVM), and Bayesian Network Classifiers (BNC) models with and without CT scan results were trained and compared to CT scan results alone and the Alvarado score using area under the Receiver Operator Curve (ROC), sensitivity, and specificity measures as well as calibration indices from 500 bootstrapped samples. RESULTS: Among the cases that underwent appendectomy, 88% had pathology-confirmed appendicitis. All the ML algorithms had better sensitivity, specificity, and ROC than the Alvarado score. SVM with and without CT had the best indices and could predict if imaging would aid in appendicitis diagnosis. CONCLUSION: This study demonstrated that SVM with and without CT results can be used for selective imaging in the diagnosis of appendicitis. This study serves as the initial step and proof-of-concept to externally validate these results with larger and more diverse patient population.

Looking Back But Leaning Forward: New Opportunities and Challenges for North Carolina's Rural Communities.

Rural North Carolina is as diverse as it is beautiful. Each community, county, and region presents a unique set of challenges and opportunities in maintaining and improving the health of its people. Forty-five years ago, Jim Bernstein and other leaders in the state understood that in order to provide access to care and equalize the chances rural North Carolinians have to thrive, a focused approach was necessary. And so it began: through the efforts of these leaders, the first Office of Rural Health was born in North Carolina. There is much to celebrate this year and there is much work ahead. Rural North Carolina is celebrated because of the engagement of rural citizens in their communities and their grit and resourcefulness in tough times and good times. This volume of the North Carolina Medical Journal (NCMJ) is dedicated to those leaders, rural providers, and their communities as they strive to make the best of whatever situation they find themselves in. This is also an opportunity for each of us to consider and learn from the past and bring our best thinking to the future.

Medical Residents' Experiences With Medically Unexplained Illness and Medically Unexplained Symptoms.

Patients who present with medically unexplained illnesses or medically unexplained symptoms (MUI/S) tend to be higher utilizers of health care services and have significantly greater health care costs than other patients, which add stress and strain for both the patient and provider. Although MUI/S are commonly seen in primary care, there is not sufficient information available regarding how providers can increase their level of confidence and decrease their level of frustration when working with patients who present with MUI/S. The goal of this article is to present findings from a qualitative phenomenology study, which highlights medical residents' experiences of caring for patients with MUI/S and the personal and professional factors that contributed to their clinical approaches. Results from these studies indicate that residents often experience a lack of confidence in their ability to effectively treat patients with MUI/S, as well as frustration surrounding their encounters with this group of patients.

The importance of health insurance and the safety net in rural communities.

Access to health insurance and health care are critical for people living in rural communities, where the safety net is fragile. However, rural communities face challenges as they enroll uninsured people in the health insurance marketplace, educate newly insured individuals on how to use insurance, and coordinate care for those who remain uninsured.

Integrated primary care: a systematic review of program characteristics.

The integration of behavioral health services into primary care medical settings may be part of the solution to the fragmented health care system currently found in the United States. Although integrated primary care (IPC) is implemented in various locations across the United States, little information is available about how IPC is specifically practiced. Using a systematic review design, we extracted data from 76 articles to examine 6 categories of IPC program characteristics, including collaboration practices (e.g., shared decision-making, written communication, hallway conversations), program models, behavioral health interventions, behavioral health training and supervision, behavioral health provider type, and setting. Findings show that most IPC programs include psychoeducation, medication, follow-up contact, psychotherapy, and at least 1 care management strategy as part of treatment. Fewer than half of researchers report communication between providers, and even fewer report collaboration as a "shared decision making process." A third of researchers report training and/or supervising behavioral health providers to work in an IPC program, and a fourth report recruiting nurses as behavioral health providers. Of all the studies, family-based interventions were used in 1. We recommend that future researchers report more information about collaboration processes as well as training and supervision of behavioral health providers. We also recommend that researchers develop IPC programs that involve family members in treatment and better implement theory into future IPC programs to support conceptualization and replication of IPC program models.