Evolving health information technology and the timely availability of visit diagnoses from ambulatory visits: A natural experiment in an integrated delivery system

<p>Abstract</p> <p>Background</p> <p>Health information technology (HIT) may improve health care quality and outcomes, in part by making information available in a timelier manner. However, there are few studies documenting the changes in timely availability of data with the use of a sophisticated electronic medical record (EMR), nor a description of how the timely availability of data might differ with different types of EMRs. We hypothesized that timely availability of data would improve with use of increasingly sophisticated forms of HIT.</p> <p>Methods</p> <p>We used an historical observation design (2004–2006) using electronic data from office visits in an integrated delivery system with three types of HIT: Basic, Intermediate, and Advanced. We calculated the monthly percentage of visits using the various types of HIT for entry of visit diagnoses into the delivery system's electronic database, and the time between the visit and the availability of the visit diagnoses in the database.</p> <p>Results</p> <p>In January 2004, when only Basic HIT was available, 10% of office visits had diagnoses entered on the same day as the visit and 90% within a week; 85% of office visits used paper forms for recording visit diagnoses, 16% used Basic at that time. By December 2006, 95% of all office visits had diagnoses available on the same day as the visit, when 98% of office visits used some form of HIT for entry of visit diagnoses (Advanced HIT for 67% of visits).</p> <p>Conclusion</p> <p>Use of HIT systems is associated with dramatic increases in the timely availability of diagnostic information, though the effects may vary by sophistication of HIT system. Timely clinical data are critical for real-time population surveillance, and valuable for routine clinical care.</p

rEHR: An R package for manipulating and analysing Electronic Health Record data

Research with structured Electronic Health Records (EHRs) is expanding as data becomes more accessible; analytic methods advance; and the scientific validity of such studies is increasingly accepted. However, data science methodology to enable the rapid searching/extraction, cleaning and analysis of these large, often complex, datasets is less well developed. In addition, commonly used software is inadequate, resulting in bottlenecks in research workflows and in obstacles to increased transparency and reproducibility of the research. Preparing a research-ready dataset from EHRs is a complex and time consuming task requiring substantial data science skills, even for simple designs. In addition, certain aspects of the workflow are computationally intensive, for example extraction of longitudinal data and matching controls to a large cohort, which may take days or even weeks to run using standard software. The rEHR package simplifies and accelerates the process of extracting ready-for-analysis datasets from EHR databases. It has a simple import function to a database backend that greatly accelerates data access times. A set of generic query functions allow users to extract data efficiently without needing detailed knowledge of SQL queries. Longitudinal data extractions can also be made in a single command, making use of parallel processing. The package also contains functions for cutting data by time-varying covariates, matching controls to cases, unit conversion and construction of clinical code lists. There are also functions to synthesise dummy EHR. The package has been tested with one for the largest primary care EHRs, the Clinical Practice Research Datalink (CPRD), but allows for a common interface to other EHRs. This simplified and accelerated work flow for EHR data extraction results in simpler, cleaner scripts that are more easily debugged, shared and reproduced

Computerized clinical decision support systems for acute care management: A decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes

<p>Abstract</p> <p>Background</p> <p>Acute medical care often demands timely, accurate decisions in complex situations. Computerized clinical decision support systems (CCDSSs) have many features that could help. However, as for any medical intervention, claims that CCDSSs improve care processes and patient outcomes need to be rigorously assessed. The objective of this review was to systematically review the effects of CCDSSs on process of care and patient outcomes for acute medical care.</p> <p>Methods</p> <p>We conducted a decision-maker-researcher partnership systematic review. MEDLINE, EMBASE, Evidence-Based Medicine Reviews databases (Cochrane Database of Systematic Reviews, DARE, ACP Journal Club, and others), and the Inspec bibliographic database were searched to January 2010, in all languages, for randomized controlled trials (RCTs) of CCDSSs in all clinical areas. We included RCTs that evaluated the effect on process of care or patient outcomes of a CCDSS used for acute medical care compared with care provided without a CCDSS. A study was considered to have a positive effect (<it>i.e.</it>, CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive.</p> <p>Results</p> <p>Thirty-six studies met our inclusion criteria for acute medical care. The CCDSS improved process of care in 63% (22/35) of studies, including 64% (9/14) of medication dosing assistants, 82% (9/11) of management assistants using alerts/reminders, 38% (3/8) of management assistants using guidelines/algorithms, and 67% (2/3) of diagnostic assistants. Twenty studies evaluated patient outcomes, of which three (15%) reported improvements, all of which were medication dosing assistants.</p> <p>Conclusion</p> <p>The majority of CCDSSs demonstrated improvements in process of care, but patient outcomes were less likely to be evaluated and far less likely to show positive results.</p

Automated Identification of Acute Hepatitis B Using Electronic Medical Record Data to Facilitate Public Health Surveillance

Automatic identification of notifiable diseases from electronic medical records can potentially improve the timeliness and completeness of public health surveillance. We describe the development and implementation of an algorithm for prospective surveillance of patients with acute hepatitis B using electronic medical record data.Initial algorithms were created by adapting Centers for Disease Control and Prevention diagnostic criteria for acute hepatitis B into electronic terms. The algorithms were tested by applying them to ambulatory electronic medical record data spanning 1990 to May 2006. A physician reviewer classified each case identified as acute or chronic infection. Additional criteria were added to algorithms in serial fashion to improve accuracy. The best algorithm was validated by applying it to prospective electronic medical record data from June 2006 through April 2008. Completeness of case capture was assessed by comparison with state health department records.A final algorithm including a positive hepatitis B specific test, elevated transaminases and bilirubin, absence of prior positive hepatitis B tests, and absence of an ICD9 code for chronic hepatitis B identified 112/113 patients with acute hepatitis B (sensitivity 97.4%, 95% confidence interval 94-100%; specificity 93.8%, 95% confidence interval 87-100%). Application of this algorithm to prospective electronic medical record data identified 8 cases without false positives. These included 4 patients that had not been reported to the health department. There were no known cases of acute hepatitis B missed by the algorithm.An algorithm using codified electronic medical record data can reliably detect acute hepatitis B. The completeness of public health surveillance may be improved by automatically identifying notifiable diseases from electronic medical record data

Fair play:Perceived fairness in crowdsourcing competitions and the customer relationship-related consequences

TeleRehab enables the rehabilitation services to be delivered in distance by providing information exchange between patient with disabilities and the clinical professionals. The readiness step in any adoption of healthcare services should always be one of the requirements for a successful implementation of an innovation. However, little scholarly has been undertaken to study its influence on TeleRehab and the various barrier factors that influence its adoption. This research explores the barrier factors that influence the readiness of healthcare institution to adopt TeleRehab. This paper presents a semi-structured interview involving 23 clinical professionals of a case study on the issues of TeleRehab readiness in one rehabilitation centre in Malaysia. By applying thematic analysis, the study uncovers seven barriers that affect the TeleRehab readiness. This includes barriers of no urgency to change, less awareness, less involvement in planning, not enough exposure on e-Healthcare knowledge, resistance to change, low usage of hardware and software, and less connectivity. The study contributes to both TeleRehab management and technology readiness research in hospitals

Do computerised clinical decision support systems for prescribing change practice? A systematic review of the literature (1990-2007)

Computerised clinical decision support systems (CDSSs) are used widely to improve quality of care and patient outcomes. This systematic review evaluated the impact of CDSSs in targeting specific aspects of prescribing, namely initiating, monitoring and stopping therapy. We also examined the influence of clinical setting (institutional vs ambulatory care), system- or user-initiation of CDSS, multi-faceted vs stand alone CDSS interventions and clinical target on practice changes in line with the intent of the CDSS. We searched Medline, Embase and PsychINFO for publications from 1990-2007 detailing CDSS prescribing interventions. Pairs of independent reviewers extracted the key features and prescribing outcomes of methodologically adequate studies (experiments and strong quasi-experiments). 56 studies met our inclusion criteria, 38 addressing initiating, 23 monitoring and three stopping therapy. At the time of initiating therapy, CDSSs appear to be somewhat more effective after, rather than before, drug selection has occurred (7/12 versus 12/26 studies reporting statistically significant improvements in favour of CDSSs on = 50% of prescribing outcomes reported). CDSSs also appeared to be effective for monitoring therapy, particularly using laboratory test reminders (4/7 studies reporting significant improvements in favour of CDSSs on the majority of prescribing outcomes). None of the studies addressing stopping therapy demonstrated impacts in favour of CDSSs over comparators. The most consistently effective approaches used system-initiated advice to fine-tune existing therapy by making recommendations to improve patient safety, adjust the dose, duration or form of prescribed drugs or increase the laboratory testing rates for patients on long-term therapy. CDSSs appeared to perform better in institutional compared to ambulatory settings and when decision support was initiated automatically by the system as opposed to user initiation. CDSSs implemented with other strategies such as education were no more successful in improving prescribing than stand alone interventions. Cardiovascular disease was the most studied clinical target but few studies demonstrated significant improvements on the majority of prescribing outcomes. Our understanding of CDSS impacts on specific aspects of the prescribing process remains relatively limited. Future implementation should build on effective approaches including the use of system-initiated advice to address safety issues and improve the monitoring of therapy