Data warehouse approach to nursing management

Junttila, Kristiina; Meretoja, Riitta; Seppälä, Anja; Tolppanen, Esa-Matti; Ala-Nikkola, Taina; Silvennoinen, Leena

doi:10.1111/j.1365-2834.2007.00690.x

Cited by 21 publications

(56 citation statements)

References 9 publications

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“…The development during the ten‐year period indicates a growing interest for performance measurements among health care actors (cf. Curtright et al, 2000; and Junttila, 2007). Even though it was hard to come up with appropriate measures in the beginning, the medical professionals within their various sub‐specialities have successively agreed upon suitable indicators for concluded treatments.…”

Section: Discussionmentioning

confidence: 97%

“…It was viewed as a common language which could be used for an all‐round elucidation of activities (cf. Junttila et al, 2007). The concept of balance was defined as a balance between the different perspectives, which the medical professionals found to be favourable.…”

Section: Discussionmentioning

confidence: 99%

“…Curtright et al (2000) describe how the implementation of the BSC leads to an increased interest for performance measurements among administrators and physicians. Junttila et al (2007) explain how the BSC resulted in visible improvements understood by nurses. At the same time the needs of patients could be compared to treatment and feedback on performance became clearer.…”

Section: Theoretical Reference Framementioning

confidence: 99%

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Measurement and Health Care Management

Aidemark

Funck

2009

Financ Acc Manag

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Over recent decades many health care organisations have ‘modernised’ their management accounting systems by using the balanced scorecard (BSC). However, public health care involves multi‐dimensional goals and resolving conflicts of interest. The question is whether a management control system based on measurements is suitable in a health care context. Light can be shed on this question using a study of the application of a BSC over a ten‐year period at Högland Hospital. The study shows that the BSC and measurements of output and behaviour became a way of approaching unanswered questions about processes and quality development. Medical professionals with management responsibility, in co‐operation with medical professionals responsible for different specialities, determined measures and targets and the measurements were integrated into activity processes and affected activities.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Theoretical Reference Framementioning

confidence: 99%

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Measurement and Health Care Management

Aidemark

Funck

2009

Financ Acc Manag

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“…To allow for efficient research analyses, EMR data must first be warehoused to enable data analyses across patient populations. [15][16][17][18][19][20][21] However, healthcare data modelling is difficult and time consuming because of the complexity of the medical knowledge involved. Thus, the most common approaches to clinical data warehouse modelling are variations on the entityattribute-value (EAV) model, [22][23][24][25][26][27][28] where data are stored in a single table with three columns: entity identification, attribute and attribute value.…”

Section: Introductionmentioning

confidence: 99%

A pragmatic method for electronic medical record-based observational studies: developing an electronic medical records retrieval system for clinical research

et al. 2012

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ObjectiveThe use of electronic medical record (EMR) data is necessary to improve clinical research efficiency. However, it is not easy to identify patients who meet research eligibility criteria and collect the necessary information from EMRs because the data collection process must integrate various techniques, including the development of a data warehouse and translation of eligibility criteria into computable criteria. This research aimed to demonstrate an electronic medical records retrieval system (ERS) and an example of a hospital-based cohort study that identified both patients and exposure with an ERS. We also evaluated the feasibility and usefulness of the method.DesignThe system was developed and evaluated.ParticipantsIn total, 800 000 cases of clinical information stored in EMRs at our hospital were used.Primary and secondary outcome measuresThe feasibility and usefulness of the ERS, the method to convert text from eligible criteria to computable criteria, and a confirmation method to increase research data accuracy.ResultsTo comprehensively and efficiently collect information from patients participating in clinical research, we developed an ERS. To create the ERS database, we designed a multidimensional data model optimised for patient identification. We also devised practical methods to translate narrative eligibility criteria into computable parameters. We applied the system to an actual hospital-based cohort study performed at our hospital and converted the test results into computable criteria. Based on this information, we identified eligible patients and extracted data necessary for confirmation by our investigators and for statistical analyses with our ERS.ConclusionsWe propose a pragmatic methodology to identify patients from EMRs who meet clinical research eligibility criteria. Our ERS allowed for the efficient collection of information on the eligibility of a given patient, reduced the labour required from the investigators and improved the reliability of the results.

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“…HHS had also initiated the development and implementation of formal clinical staffing plans. The development of dashboards and staffing plans relied on previous work, as well as a wealth of academic research (Donaldson et al 2005, Doran 2003, Egan 2006, Junttila et al 2007, Lowe and Baker 1997, Mazzella-Ebstein and Saddul 2004, Park and Huber 2007, Rosow et al 2003, Saint-Eloi et al 2005, Urden 1996, Whitman et al 2001, Fitzpatrick 2002, Mills and Walters 2006, O'Brien-Pallas et al 2002, Sangster 2007 This project, Linking Nursing Outcomes, Workload and Staffing Decisions, responded directly to the recommendation for a pilot demonstration project to determine what indicators were needed and useful to assess the level of staffing on an individual unit required to provide high-quality patient care. This project builds on and enhances initiatives already underway in the province.…”

Section: Introductionmentioning

confidence: 99%

Ontario: Linking Nursing Outcomes, Workload and Staffing Decisions in the Workplace: The Dashboard Project

Fram¹,

Morgan²

2012

cjnl

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Research shows that nurses want to provide more input into assessing patient acuity, changes in patient needs and staffing requirements. the Dashboard project involved the further development and application of an electronic monitoring tool that offers a single source of nursing, patient and organizational information. it is designed to help inform nurse staffing decisions within a hospital setting. the Dashboard access link was installed in computers in eight nursing units within the hamilton health sciences (hhs) network. the Dashboard indicators are populated from existing information/patient databases within the Decision support Department at hhs. committees composed of the unit manager, staff nurses, project coordinator, financial controller and an information controller met regularly to review the Dashboard indicators. participants discussed the ability of the indicators to reflect their patients' needs and the feasibility of using the indicators to inform their clinical staffing plans.

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Data warehouse approach to nursing management

Cited by 21 publications

References 9 publications

Measurement and Health Care Management

Measurement and Health Care Management

A pragmatic method for electronic medical record-based observational studies: developing an electronic medical records retrieval system for clinical research

Ontario: Linking Nursing Outcomes, Workload and Staffing Decisions in the Workplace: The Dashboard Project

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