Study of Incompleteness Medical Records of Inpatient in Appendicitis Cases at Dr. Reksodiwiryo Hospital Padang

Background: Incomplete filling of medical record files for inpatients at Dr. Reksodiwiryo hospital medical records will be describe health services and the quality of medical record services. Medical record quality services include the completeness of medical record files, accuracy in providing diagnosis and diagnosis codes, as well as speed in providing service information. The requirements for quality medical records must be accurate, complete, reliable, valid, timely, usable, common, comparable, guaranteed, and easy.Methods: This research method is a descriptive with a retrospective approach or looking at existing data. This study was carried out in September 2021. The population was 70 files cases of inpatient digestive surgery. Samples were taken from 27 files of inpatients with appendicitis cases.Results: From the research that has been done, the highest percentage of incomplete identification components is found on the gender item about 81.48%, the highest percentage of incomplete important report components is obtained on the medical resume and informed consent items about 11.1%. The highest percentage of incomplete authentication components was obtained in the nursing degree about 96.3%. The highest percentage of the components of the recording method was obtained by 59.3%, there are several blank sections about 16 files. The percentage of incomplete diagnostic codes and procedures is 100% Conclusions: the researcher suggested that the hospital can have an Operational Standart on filling out the completeness of medical records files

Accuracy and Validity Outpatient Diagnosis Code Base On ICD-10 at Imogiri I Health Center Bantul Yogyakarta

Background: Analysis of accuracy and validity fill code diagnosis on medical record document is very important because if diagnosis code is not appropriate with ICD-10, will cause decline in quality services health center, generated data have this validation data level is low, because accuracy code very important for health center such as index process and statistical report, as basis for making outpatient morbidity report and top ten diseases reports, as well as influencing policies will be taken by primary health center management. This study aims to analyze accuracy and validity diagnosis disease code based on ICD-10 fourth quarter in 2020 Imogiri I Health Center Bantul.Methods: Descriptive qualitative approach, case study design. Subject is a doctor, nurse, head record medical and staff. Object is outpatients medical record document in Imogiri I Health Center Bantul. Total sample 99 medical record file. Obtaining data from this study through interviews and observations.Results: Number of complete accurate diagnosis codes is 60 (60,6%), incomplete accurate diagnosis codes is 26 (26.3%) and inaccurate diagnosis codes is 13 (13.1%). Inaccuracies include errors in determining code, errors in determining 4th character ICD-10 code, not adding 4th and 5th characters, not including external cause, and multiple diseases.Conclusions: Inaccuracy factors are not competence medical record staff, incomplete diagnosis writing and no training, no evaluation or coding audit has been carried out, and standard operational procedure is not socialized.

Prevalence of comorbidities and concomitant medication use in acromegaly: analysis of real-world data from the United States

Purpose Patients receiving treatment for acromegaly often experience significant associated comorbidities for which they are prescribed additional medications. We aimed to determine the real-world prevalence of comorbidities and concomitant medications in patients with acromegaly, and to investigate the association between frequency of comorbidities and number of concomitantly prescribed medications. Methods Administrative claims data were obtained from the IBM® MarketScan® database for a cohort of patients with acromegaly, identified by relevant diagnosis codes and acromegaly treatments, and a matched control cohort of patients without acromegaly from January 2010 through April 2020. Comorbidities were identified based on relevant claims and assessed for both cohorts. Results Overall, 1175 patients with acromegaly and 5875 matched patients without acromegaly were included. Patients with acromegaly had significantly more comorbidities and were prescribed concomitant medications more so than patients without acromegaly. In the acromegaly and control cohorts, respectively, 67.6% and 48.4% of patients had cardiovascular disorders, the most prevalent comorbidities, and 89.0% and 68.3% were prescribed > 3 concomitant medications (p < 0.0001). Hypopituitarism and hypothalamic disorders, sleep apnea, malignant neoplasms and cancer, and arthritis and musculoskeletal disorders were also highly prevalent in the acromegaly cohort. A moderate, positive correlation (Spearman correlation coefficient 0.60) was found between number of comorbidities and number of concomitant medications in the acromegaly cohort. Conclusion Compared with patients without acromegaly, patients with acromegaly have significantly more comorbidities and are prescribed significantly more concomitant medications. Physicians should consider the number and type of ongoing medications for individual patients before prescribing additional acromegaly treatments.

Risk of Myopericarditis following COVID-19 mRNA vaccination in a Large Integrated Health System: A Comparison of Completeness and Timeliness of Two Methods

Purpose: How completely do hospital discharge diagnoses identify cases of myopericarditis after an mRNA vaccine? Methods: We assembled a cohort 12 to 39 years old patients, insured by Kaiser Permanente Northwest, who received at least one dose of an mRNA vaccine (Pfizer BioNTech or Moderna) between December 2020 and October 2021. We followed them for up to 30 days after their second dose of an mRNA vaccine to identify encounters for myocarditis, pericarditis or myopericarditis. We compared two identification methods: A method that searched all encounter diagnoses using a brief text description (e.g., ICD-10-CM code I40.9 is defined as acute myocarditis, unspecified). We searched the text description of all inpatient or outpatient encounter diagnoses (in any position) for myocarditis or pericarditis. The other method was developed by the Centers for Disease Control and Preventions Vaccine Safety Datalink (VSD), which searched for emergency department visits or hospitalizations with a select set of discharge ICD-10-CM diagnosis codes. For both methods, two physicians independently reviewed the identified patient records and classified them as confirmed, probable or not cases using the CDCs case definition. Results: The encounter methodology identified 14 distinct patients who met the confirmed or probable CDC case definition for acute myocarditis or pericarditis with an onset within 21 days of receipt of COVID-19 vaccination. Three of these 14 patients had an ICD-10 code of I51.4 Myocarditis, Unspecified which was overlooked by the VSD algorithm. The VSD methodology identified 11 patients who met the CDC case definition for acute myocarditis or pericarditis. Seven (64%) of the eleven patients had initial care for myopericarditis outside of a KPNW facility and their diagnosis could not be ascertained by the VSD methodology until claims were submitted (median delay of 33 days; range of 12-195 days). Among those who received a second dose of vaccine (n=146,785), we estimated a risk as 95.4 cases of myopericarditis per million second doses administered (95% CI, 52.1 to 160.0). Conclusion: We identified additional valid cases of myopericarditis following an mRNA vaccination that would be missed by the VSDs search algorithm, which depends on select hospital discharge diagnosis codes. The true incidence of myopericarditis is markedly higher than the incidence reported to US advisory committees. The VSD should validate its search algorithm to improve its sensitivity for myopericarditis.

Rural and Urban Difference in Longitudinal Trends in Prevalence of Dementia in Medicare Claims and Survey Data

Shortage of physicians in rural areas can lead to lower diagnosis and underestimation of dementia prevalence in these communities. We used data from the nationally representative Health and Retirement Study and a 20-percent sample of Medicare claims to study rural-urban differences in dementia prevalence. The survey dementia diagnosis is free from medical assessment while the claims diagnosis needs a physician diagnosis. We estimated the trends in dementia prevalence from (2002-2016) based on cognitive tests (using survey data) and diagnosis codes (using claims data) utilizing ordinary least squares regression. Dementia prevalence based on diagnosis codes declined in both urban and rural areas over the course of the study, with a sharper decline in urban areas. Dementia prevalence using diagnosis codes showed significantly higher rates in urban areas during all years (0.024 vs 0.018 in 2002 and 0.017 vs 0.013 in 2014 in rural vs urban areas, respectively). Dementia in the cognitive test sample was higher in rural areas (0.11 vs 0.08 in 2000 and 0.08 vs 0.7 in 2014 in rural vs urban areas), a difference that was significant only in 2004. Our results indicate lower dementia prevalence rates in rural areas in claims based sample compared to survey sample which its dementia prevalence is free medical assessment. Claims data are valuable sources for tracking dementia in the US population, however they are based on medical diagnosis.In rural areas, where there is shortage of physicians and a lack of access to health care services, claims based studies may underestimate dementia rates.

Empirical Assessment of Alternative Methods for Identifying Seasonality in Observational Healthcare Data

BackgroundSeasonality classification is a well-known and important part of time series analysis. Understanding the seasonality of a biological event can contribute to an improved understanding of its causes and help guide appropriate responses. Observational data, however, are not comprised of biological events, but timestamped diagnosis codes the combination of which (along with additional requirements) are used as proxies for biological events. As there exist different methods for determining the seasonality of a time series, it is necessary to know if these methods exhibit concordance. In this study we seek to determine the concordance of these methods by applying them to time series derived from diagnosis codes in observational data. Methods: We compared 8 methods for determining the seasonality of a time series at three levels of significance (0.01, 0.05, and 0.1), against 10 observational health databases. We evaluated 61,467 time series at each level of significance, totaling 184,401 evaluations. Results: Methods of binary seasonality classification when applied to time series derived from diagnosis codes in observational health data produce inconsistent results. Across all databases and levels of significance, concordance ranged from 20.2% to 40.2%.Conclusion: The results indicate that researchers relying on automated methods to assess the seasonality of time series derived from diagnosis codes in observational data should be aware that the methods are not interchangeable. Seasonality determination is highly dependent on the method chosen.

Abstract 11759: Survival Among Medicare Beneficiaries After Out-of-Hospital Cardiac Arrest Undergoing Interhospital Transfer

Intro: Among individuals treated for out-of-hospital cardiac arrest (OHCA), there is hospital-level variability in mortality, but the relationship between interhospital transfer (IHT) OHCA volume and survival remain unclear. We sought to examine the association of OHCA volume and survival for individuals undergoing IHT. Methods: Utilizing age-eligible Medicare fee-for-service claims, we identified an emergency department treated OHCA cohort using ICD-9/10 diagnosis codes. Hospital OHCA volume was defined as the total number of index (first-treated) OHCA claims during the study period and were binned into quartiles. Each claim was assigned the OHCA volume quartile of the index hospital and the index volume of the receiving hospital. Multiple logistic regression was conducted to assess the association between initial and receiving hospital volume categories and survival to 30 days among IHT patients while controlling for patient-level characteristics (age, sex, race), comorbidity index, urbanicity of index hospital and days to transfer. Results: We identified a cohort of 222,018 claims at 4,461 hospitals between 1/2013-12/2015. Median age was 78 years (IQR 71-85); 44% were female; 11% of the cohort was alive at 30 days. IHT occurred in 12,245 cases (5.5%) and 59% of transfers occurred on the day of admission or day 1. Transfers originated from 3411 index hospitals and 1566 receiving hospitals. Median OHCA hospital index volume was 25 [IQR 9, 67]. Adjusted odds of survival at 30 days was significantly lower at index hospitals with lower OHCA volumes compared to the highest volume category (aOR [95%CI] Q2: 0.71 [0.6, 0.83] p<0.001). Additionally, odds of survival at 30 days was significantly lower at low volume receiving hospitals (aOR [95%CI] Q1: 0.73 [0.55, 0.99] p<0.001), and increased for higher OHCA volume receiving hospitals, but these groups did not achieve statistical significance. Conclusion: For Medicare beneficiaries who suffer an OHCA and undergo IHT, lower index and receiving hospital OHCA volume was significantly associated with decreased adjusted odds of 30-day survival. Further exploration of hospital characteristics, timing, and transfer patterns is needed to understand differences in benefit for OHCA patients undergoing IHT.