Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus

Objective

To evaluate data fragmentation across healthcare centers with regard to the accuracy of a high-throughput clinical phenotyping (HTCP) algorithm developed to differentiate (1) patients with type 2 diabetes mellitus (T2DM) and (2) patients with no diabetes.

Materials and methods

This population-based study identified all Olmsted County, Minnesota residents in 2007. We used provider-linked electronic medical record data from the two healthcare centers that provide >95% of all care to County residents (ie, Olmsted Medical Center and Mayo Clinic in Rochester, Minnesota, USA). Subjects were limited to residents with one or more encounter January 1, 2006 through December 31, 2007 at both healthcare centers. DM-relevant data on diagnoses, laboratory results, and medication from both centers were obtained during this period. The algorithm was first executed using data from both centers (ie, the gold standard) and then from Mayo Clinic alone. Positive predictive values and false-negative rates were calculated, and the McNemar test was used to compare categorization when data from the Mayo Clinic alone were used with the gold standard. Age and sex were compared between true-positive and false-negative subjects with T2DM. Statistical significance was accepted as p<0.05.

Results

With data from both medical centers, 765 subjects with T2DM (4256 non-DM subjects) were identified. When single-center data were used, 252 T2DM subjects (1573 non-DM subjects) were missed; an additional false-positive 27 T2DM subjects (215 non-DM subjects) were identified. The positive predictive values and false-negative rates were 95.0% (513/540) and 32.9% (252/765), respectively, for T2DM subjects and 92.6% (2683/2898) and 37.0% (1573/4256), respectively, for non-DM subjects. Age and sex distribution differed between true-positive (mean age 62.1; 45% female) and false-negative (mean age 65.0; 56.0% female) T2DM subjects.

Conclusion

The findings show that application of an HTCP algorithm using data from a single medical center contributes to misclassification. These findings should be considered carefully by researchers when developing and executing HTCP algorithms.     

老年糖调节受损患者合并心脑血管疾病的危险因素分析

目的探讨老年糖调节受损(IGR)患者心脑血管病变的特点及相关危险因素。方法通过回顾性分析,将114例老年IGR患者分为心脑血管病变组(病变组)61例和无血管病变组(无病变组)53例。病变组包括缺血性心脏病(IHD)患者35例、脑血管病变(CVD)患者26例。对2组患者的临床数据进行比较及回归分析。结果病变组的年龄、高血压患病率、病程、C反应蛋白水平比无病变组明显增高(P<0.05);各亚组与无病变组的比较也有相似的趋势。回归分析显示,年龄、高血压是老年IGR患者心脑血管病变的独立危险因素,同时也是IHD和CVD的独立危险因素。另外高三酰甘油血症与心脑血管病变及IHD分别独立相关。结论对于老年IGR患者,除了年龄、高血压外,高三酰甘油血症是心脑血管病变的独立危险因子。     

社区护士对糖尿病患者营养健康教育干预效果评价

目的了解社区护士对糖尿病患者进行营养健康教育后的成效,为进一步制定干预措施提供依据。方法采用问卷调查和实验室检测的方法,随机抽取2型糖尿病患者160例,分成两组,实验组60例,对照组100例,实验组糖尿病患者由社区护士进行为期2年的营养健康教育干预,对照组不采取任何干预措施。比较干预后两组营养健康知识调查问卷得分及各实验室检测指标。结果实验组营养健康知识调查问卷得分明显高于对照组（P〈0.01）;生理指标：两组体重、体重指数、空腹血糖、三酰甘油、餐后血糖和糖化血红蛋白值比较,差异有统计学意义（P〈0.05）,实验组优于对照组;营养素指标：两组能量、维生素A、维生素B1、维生素B6、维生素B12、烟酸、叶酸、维生素D、磷、钾、锌、硒、铜和生物素等16项指标比较,差异均有统计学意义（P〈0.05）,实验组摄入量明显优于对照组。结论社区护士对糖尿病患者的营养健康教育方式卓有成效,值得推广。     

2 型糖尿病合并冠心病患者氯吡格雷抵抗的危险因素

目的观察老年2型糖尿病合并冠心病患者发生氯吡格雷抵抗的情况,探讨分析相应的危险因素。方法选择本院2011年7月—2013年7月期间收治的76例老年2型糖尿病合并冠心病患者作为观察对象,根据血小板聚集试验结果进行分组,其中血小板抑制率〉50％共40例（对照组）,〈50％共36例（观察组）,对比2组流行病学、脂代谢、糖代谢与超敏C反应蛋白等水平值。结果2组患者年龄、性别、体重、高脂血症与高血压发病史以及吸烟史等流行病学指标对比,差异均不具有统计学意义（均P〉0．05）;观察组空腹血糖、超敏C反应蛋白显著高于对照组,而高密度脂蛋白胆固醇显著低于对照组,差异均具有统计学意义（均P〈0．05）;经多因素分析后,高密度脂蛋白胆固醇、空腹血糖、超敏c反应蛋白均是影响患者发生氯吡格雷抵抗的危险因素（均P〈0．05）。结论老年2型糖尿病合并冠心病患者出现氯吡格雷抵抗的发生率大,且与患者的空腹血糖水平、高密度脂蛋白胆固醇、超敏C反应蛋白等因素有关,值得临床上进一步分析。     

The Triangle Model for evaluating the effect of health information technology on healthcare quality and safety

With the proliferation of relatively mature health information technology (IT) systems with large numbers of users, it becomes increasingly important to evaluate the effect of these systems on the quality and safety of healthcare. Previous research on the effectiveness of health IT has had mixed results, which may be in part attributable to the evaluation frameworks used. The authors propose a model for evaluation, the Triangle Model, developed for designing studies of quality and safety outcomes of health IT. This model identifies structure-level predictors, including characteristics of: (1) the technology itself; (2) the provider using the technology; (3) the organizational setting; and (4) the patient population. In addition, the model outlines process predictors, including (1) usage of the technology, (2) organizational support for and customization of the technology, and (3) organizational policies and procedures about quality and safety. The Triangle Model specifies the variables to be measured, but is flexible enough to accommodate both qualitative and quantitative approaches to capturing them. The authors illustrate this model, which integrates perspectives from both health services research and biomedical informatics, with examples from evaluations of electronic prescribing, but it is also applicable to a variety of types of health IT systems.     

Retrieval of diagnostic and treatment studies for clinical use through PubMed and PubMed's Clinical Queries filters

Objective

Clinical Queries filters were developed to improve the retrieval of high-quality studies in searches on clinical matters. The study objective was to determine the yield of relevant citations and physician satisfaction while searching for diagnostic and treatment studies using the Clinical Queries page of PubMed compared with searching PubMed without these filters.

Materials and methods

Forty practicing physicians, presented with standardized treatment and diagnosis questions and one question of their choosing, entered search terms which were processed in a random, blinded fashion through PubMed alone and PubMed Clinical Queries. Participants rated search retrievals for applicability to the question at hand and satisfaction.

Results

For treatment, the primary outcome of retrieval of relevant articles was not significantly different between the groups, but a higher proportion of articles from the Clinical Queries searches met methodologic criteria (p=0.049), and more articles were published in core internal medicine journals (p=0.056). For diagnosis, the filtered results returned more relevant articles (p=0.031) and fewer irrelevant articles (overall retrieval less, p=0.023); participants needed to screen fewer articles before arriving at the first relevant citation (p<0.05). Relevance was also influenced by content terms used by participants in searching. Participants varied greatly in their search performance.

Discussion

Clinical Queries filtered searches returned more high-quality studies, though the retrieval of relevant articles was only statistically different between the groups for diagnosis questions.

Conclusion

Retrieving clinically important research studies from Medline is a challenging task for physicians. Methodological search filters can improve search retrieval.     

The impact of the electronic medical record on structure,process, and outcomes within primary care: a systematic review of the evidence

Background

The electronic medical record (EMR)/electronic health record (EHR) is becoming an integral component of many primary-care outpatient practices. Before implementing an EMR/EHR system, primary-care practices should have an understanding of the potential benefits and limitations.

Objective

The objective of this study was to systematically review the recent literature around the impact of the EMR/EHR within primary-care outpatient practices.

Materials and methods

Searches of Medline, EMBASE, CINAHL, ABI Inform, and Cochrane Library were conducted to identify articles published between January 1998 and January 2010. The gray literature and reference lists of included articles were also searched. 30 studies met inclusion criteria.

Results and discussion

The EMR/EHR appears to have structural and process benefits, but the impact on clinical outcomes is less clear. Using Donabedian''s framework, five articles focused on the impact on healthcare structure, 21 explored healthcare process issues, and four focused on health-related outcomes.     

Effects of an online personal health record on medication accuracy and safety: a cluster-randomized trial

Objective

To determine the effects of a personal health record (PHR)-linked medications module on medication accuracy and safety.

Design

From September 2005 to March 2007, we conducted an on-treatment sub-study within a cluster-randomized trial involving 11 primary care practices that used the same PHR. Intervention practices received access to a medications module prompting patients to review their documented medications and identify discrepancies, generating ‘eJournals’ that enabled rapid updating of medication lists during subsequent clinical visits.

Measurements

A sample of 267 patients who submitted medications eJournals was contacted by phone 3 weeks after an eligible visit and compared with a matched sample of 274 patients in control practices that received a different PHR-linked intervention. Two blinded physician adjudicators determined unexplained discrepancies between documented and patient-reported medication regimens. The primary outcome was proportion of medications per patient with unexplained discrepancies.

Results

Among 121 046 patients in eligible practices, 3979 participated in the main trial and 541 participated in the sub-study. The proportion of medications per patient with unexplained discrepancies was 42% in the intervention arm and 51% in the control arm (adjusted OR 0.71, 95% CI 0.54 to 0.94, p=0.01). The number of unexplained discrepancies per patient with potential for severe harm was 0.03 in the intervention arm and 0.08 in the control arm (adjusted RR 0.31, 95% CI 0.10 to 0.92, p=0.04).

Conclusions

When used, concordance between documented and patient-reported medication regimens and reduction in potentially harmful medication discrepancies can be improved with a PHR medication review tool linked to the provider''s medical record.

Trial registration number

This study was registered at ClinicalTrials.gov ({"type":"clinical-trial","attrs":{"text":"NCT00251875","term_id":"NCT00251875"}}NCT00251875).     

Design and implementation of an automated email notification system for results of tests pending at discharge

Physicians are often unaware of the results of tests pending at discharge (TPADs). The authors designed and implemented an automated system to notify the responsible inpatient physician of the finalized results of TPADs using secure, network email. The system coordinates a series of electronic events triggered by the discharge time stamp and sends an email to the identified discharging attending physician once finalized results are available. A carbon copy is sent to the primary care physicians in order to facilitate communication and the subsequent transfer of responsibility. Logic was incorporated to suppress selected tests and to limit notification volume. The system was activated for patients with TPADs discharged by randomly selected inpatient-attending physicians during a 6-month pilot. They received approximately 1.6 email notifications per discharged patient with TPADs. Eighty-four per cent of inpatient-attending physicians receiving automated email notifications stated that they were satisfied with the system in a brief survey (59% survey response rate). Automated email notification is a useful strategy for managing results of TPADs.