How To Get Left Click Input In Pygame

The mouse module has capabilities that lets us verify and set the situation of the mouse in addition to verify the buttons pressed. The operate pygame.mouse.get_pressed() returns a tuple tuple representing if the mouse buttons (left, mouse-wheel, right) is being pressed or not. In order to verify regardless of whether the consumer has chosen a particle, we have to know the place they've clicked and we do that through the use of pygame.mouse.get_pos().

As the identify suggests, this operate returns the place of the mouse within the Pygame window as an x, y coordinate. For now we solely have to know the place of the mouse when the consumer clicks. We experiment this by monitoring Pygame occasions as we did in tutorial 1 employing pygame.event.get(). Since we're already calling this function, trying to find QUIT events, we'd as nicely seek for MOUSEBUTTONDOWN occasions on the identical time. This pygame.mouse.get_pressed() operate returns the Boolean worth representing the state of the mouse buttons. A True worth means the mouse button is being pressed on the time of the operate call.

This perform returns a tuple representing counting on mouse buttons (left, mouse-wheel, right) being pressed. Earlier, you noticed that pygame.event.get() returns an inventory of the occasions within the occasion queue, which you scan for KEYDOWN occasion types. Pygame additionally can grant pygame.event.get_pressed(), which returns a dictionary containing all of the present KEYDOWN occasions within the queue. To ship a digital mouse click on on on to your computer, name the pyautogui.click() method. By default, this click on on on makes use of the left mouse button and takes place wherever the mouse cursor is presently located.

We retailer the worth returned by the perform in variable 'mouse_button', which is a tuple. The index for left button is '0', the index for wheel is '1', and the index for top button is '2'. There are two essential methods to get the state of any enter device. Every time a key or button is pressed or released, or the mouse is moved, an occasion is added to the occasion queue.

You should empty this occasion queue out every body by both calling pygame.event.get() or pygame.event.pump(). To manage our occasions we basically loop using the queue, test what sort it can be after which carry out some action. This code will test if the consumer has pressed the shut button on the highest nook of the display, and in that case terminate the program.

Below is a program instance the way to make use of the keyboard occasions and keyboard functions. The instance makes use of the keyboard arrow keys to maneuver the picture on the screen. In the subsequent section, we'll write a program to make use of the mouse capabilities and mouse occasions to maneuver a picture on the screen. Pygame.event.wait() operate returns the present occasion on the queue. If there are not any messages ready on the queue, this can not return till one is available. To get all of the mouse occasions , you need to make use of the operate pygame.event.wait().

The mouse module has capabilities that enable us to ascertain which mouse button is clicked and enables us discover the situation of the mouse. As you noticed within the 'Display an Image' app in Pygame Theory of Operation chapter, the principle loop runs continuously. Responding to the occasions is completed by including event-handling code to the principle loop . This event-handling code then repeatedly displays the user's inputs and responds to the events. To discover what an enter gadget (keyboard, mouse, etc.) is doing, this system checks the state of the enter gadget by making a name to the next functions.

Now that we understand how one can show the graphic of an object, allow us to create an app to maneuver that image. The software within the subsequent few sections will transfer the ball we displayed within the past application. The ball is moved because of some key presses or mouse movements, or mouse clicks.

These consumer actions are known as events, which makes our app interactive, that's our sport or app responds to consumer inputs from the keyboard and mouse. As proven within the desk above, MOUSEBUTTON occasion kind has a further attribute 'pos'. To discover the place of the mouse, we name the operate pygame.mouse.get_pos() and keep the returned worth in variable 'mouse_pos'. The 'pos' attribute has the mouse coordinates in pixels.

The variable 'mouse_pos' incorporates the coordinates of the mouse place and is printed. The pygame key module has a operate referred to as pygame.key.get_pressed(). This operate returns the state of all keyboard buttons. The operate returns a Boolean worth displaying the state of each keyboard key. If a key's pressed, it returns a Boolean worth of '1' or 'TRUE' which means that key's pressed. For all keys that aren't pressed, this operate returns a Boolean worth of '0' or 'FALSE'.

Pygame.event module has a perform referred to as pygame.event.get(). We can discover which occasions have occurred by calling pygame.event.get() function, which returns an inventory ofpygame.event.Eventobjects within the occasions queue. As the occasion occurs as a result of user's input, they're put in a queue. Calling Pygame.event.get() perform will get occasions from the occasion queue. These occasions are returned as an inventory of Event objects.

Calling the above capabilities tells us the state of the enter machine within the meanwhile you identify the function. The event-handling code within the occasion loop will then get the occasions comparable to key pressed or mouse click on or mouse movement after which reply to the occasions detected. Since this system occasion loop loops repeatedly and is checking for events, it should reply counting on the kind of the event. You will see how this system responds in an actual program instance later on this chapter. Call setMouseCallback() to register the mouse callback operate that we outlined above, and (drawRectangle()) for mouse occasions that show up within the named window. Pygame.mouse.get_pressed() — Like pygame.key.get_pressed(), returns the state of every mouse button.

The worth returned is a tuple of measurement three that corresponds to the left, middle, and correct buttons. Pygame.mouse.get_pressed() returns an inventory of Boolean values ​​that symbolize the state of all mouse buttons. The state of a button is True so lengthy as a button is held down. Key presses, mouse movements, and even joystick actions are many of the methods wherein a consumer can grant input.

All consumer enter ends in an occasion being generated. Events can take place at any time and sometimes originate outdoors the program. All occasions in pygame are positioned within the occasion queue, which might then be accessed and manipulated. Dealing with occasions is known as dealing with them, and the code to take action known as an occasion handler. Your GUI automation packages don't must click on and sort blindly.

PyAutoGUI has screenshot functions which could create a picture file based mostly on the present contents of the screen. These functions may additionally return a Pillow Image object of the present screen's appearance. If you've been skipping spherical on this book, you'll wish to learn Chapter 17 and deploy the pillow module earlier than persevering with with this section.

You have to see the mouse pointer transfer to close the top-left nook of your display and click on on on on on on once. A full "click" is outlined as pushing a mouse button down after which releasing it to come back up with no transferring the cursor. You can even carry out a click on on on on on by calling pyautogui.mouseDown(), which solely pushes the mouse button down, and pyautogui.mouseUp(), which solely releases the button. These capabilities have the identical arguments as click(), and in fact, the click() carry out is solely a handy wrapper spherical these two carry out calls. Get_pressed() returns an inventory of Boolean values ​​that symbolize the state of all mouse buttons.

The perform pygame.mouse.get_pos will get the mouse curser position. This perform returns the mouse x and y cursor situation relative to the left-top nook of the display. Pygame.event.get() will return an inventory of all of the occasions because the final time you emptied the queue. The option to manage these occasions is determined by the kind of occasion itself.

The sort of occasion will be checked by studying the event.type field. Examples of just about every sort of prevalent occasion will be seen within the prolonged code pattern below. There are extra types, however they're quite uncommon. For many functions this will be a far extra advantageous solution to manage mouse and keyboard events. The occasion loop is designed to search for random occasions occurring every body and focus on them appropriately. Luckily, pygame doesn't prohibit you to making use of solely the occasion sorts it has defined.

You can outline your personal occasions to deal with as you see fit. In pygame, all the things is seen on a single user-created display, which may be a window or a full screen. The monitor is created employing .set_mode(), which returns a Surface representing the noticeable component to the window. You can transfer the mouse cursor across the display and simulate mouse clicks, keystrokes, and keyboard shortcuts with PyAutoGUI. You may even give PyAutoGUI a screen-shot and let it work out the coordinates of the world you ought to click. The sort of the occasion could very well be checked by examining the event.type field.

There are three kinds of mouse occasions in pygame MOUSEMOTION, MOUSEBUTTONDOWN and MOUSEBUTTONUP. Pygame will register these occasions when a display mode has been set. You create the display to make use of by calling pygame.display.set_mode() and passing a tuple or record with the specified width and height. In this case, the window is 800x600, as outlined by the constants SCREEN_WIDTH and SCREEN_HEIGHT on strains 20 and 21. This returns a Surface which represents the within dimensions of the window.

This is the portion of the window you could control, whilst the OS controls the window borders and title bar. All of pygame's mouse events, MOUSEBUTTONDOWN, MOUSEBUTTONUP, and MOUSEMOTION include attributes .pos for the mouse situation and .relpos . Button values are zero for left button, 1 for appropriate button, 2 for the center button.

The return is true when the mouse button is pressed down, and false when released. Next we've got to bind this mouse callback operate to OpenCV window. In the primary loop, we could always set a keyboard binding for key 'm' to toggle between rectangle and circle. I desired to tug and draw the Rectangle once we click on and drag left mouse button.

The Left Click creates a small new rectangle the place ever the cursor is current and Dragging will enhance rectangle size. Once done, Later, we will enhance its measurement from its borders on dragging. At last, once we name a function, the operate has been referred to as by onclick() method. Whereby, the operate might be executed solely when the left button on a mouse is clicked on a turtle. We will not be mentioning a mouse button explicitly for the reason that by default left button might be taken in as an argument for btn parameter.

To decide the mouse's existing position, we use the statement, pyautogui. This operate returns a tuple of the place of the mouse's cursor. The first worth is the x-coordinate of the place the mouse cursor is. This code will produce a left mouse click on on on on the present place of the mouse cursor. Now to deal with mouse clicks we're going to create a brand new operate referred to as "on_mouse_press". This operate might be referred to as each time consumer clicks a mouse button.

Pygame will register all occasions from the consumer into an occasion queue which may be acquired with the code pygame.event.get(). Every factor on this queue is an Event object and they will all have the attribute type, which is an integer representing what sort of occasion it is. In the pygame module there are predefined integer constants representing the type.

Except for this attribute, occasions have diverse attributes. When the participant presses a keyboard key, clicks the mouse, or strikes the mouse, apygame.event.Eventobject is created. The get_pos() perform returns the coordinates of the mouse cursor on the screen.

This piece of code goes instantly into the sport loop. You can both keep this right into a list, or use the under format to keep every worth in a separate variable. We shall be employing the pynput module to take heed to mouse events. To deploy this module execute pip deploy pynput in cmd.

Watch the output to ensure no errors have occurred; it'll inform you when the module has been efficiently installed. Here we specify an argument within the definition of the callback operate ('evt' on this case). When the operate is named as a consequence of a specified occasion happening, VPython sends the operate details concerning the event.

The identify of the argument needn't be 'evt'; use anything identify you like. When you be given a mouse click on event, you get the situation by calling pygame. You can then test for a collision between a rect centered on the mouse situation and your sprite's rect by calling pygame. To do this, you used a subset of the pygame modules, such as the display, mixer and music, time, image, event, and key modules. You additionally used a number of pygame classes, such as Rect, Surface, Sound, and Sprite.

But these solely scratch the floor of what pygame can do! Check out the official pygame documentation for a full listing of obtainable modules and classes. For this tutorial, you'll use a way referred to as .spritecollideany(), which is learn as "sprite collide any." This way accepts a Sprite and a Group as parameters. It seems at each object within the Group and checks if its .rect intersects with the .rect of the Sprite. This is ideal for this video video sport since it's worthwhile to ascertain if the solely participant collides with one in each of a Group of enemies. In programming terms, a sprite is a 2D illustration of a factor on the screen.

Hours Per Patient Day Formula

To assess the key variables used in research on nurse staffing and patient outcomes from the perspective of an international panel. A Delphi survey (November 2005-February 2006) of a purposively-selected expert panel from 10 countries consisting of 24 researchers specializing in nurse staffing and quality of health care and 8 nurse administrators. Each participant was sent by e-mail an up-to-date review of all evidence related to 39 patient-outcome, 14 nurse-staffing and 31 background variables and asked to rate the importance/usefulness of each variable for research on nurse staffing and patient outcomes. In two subsequent rounds the group median, mode, frequencies, and earlier responses were sent to each respondent. Twenty-nine participants responded to the first round (90.6%), of whom 28 (87.5%) responded to the second round.

The Delphi panel generated 7 patient-outcome, 2 nurse-staffing and 12 background variables in the first round, not well-investigated in previous research, to be added to the list. At the end of the second round the predefined level of consensus (85%) was reached for 32 patient outcomes, 10 nurse staffing measures and 29 background variables. The highest consensus levels regarding measure sensitivity to nurse staffing were found for nurse perceived quality of care, patient satisfaction and pain, and the lowest for renal failure, cardiac failure, and central nervous system complications. Nursing Hours per Patient Day received the highest consensus score as a valid measure of the number of nursing staff.

As a skill mix variable the proportion of RNs to total nursing staff achieved the highest consensus level. Both age and comorbidities were rated as important background variables by all the respondents. These results provide a snapshot of the state of the science on nurse-staffing and patient-outcomes research as of 2005. The results portray an area of nursing science in evolution and an understanding of the connections between human resource issues and healthcare quality based on both empirical findings and opinion. The NHPPD is calculated by dividing the total number of productive hours provided by all nursing staff , licensed practical nurses , and unlicensed assistive personnel with direct care responsibilities by patient days.

The NHPPD is the most widely used nurse staffing measure in most studies (Min & Scott, 2016;Van den Heede et al., 2009). Skill mix is defined as the proportion of total nursing hours provided by RNs, compared to LPNs, and UAPs available for patient care during a nursing shift . The shift to value-based care is just one of many fundamental changes happening in healthcare today.

Healthcare organizations across the continuum are challenged to increase productivity AND reduce costs while maintaining proper staff levels to meet patient needs and compliance requirements. Hours per patient day is a common industry expression used to trend the total number of direct nursing care hours , compared to the number of patients as the HPPD ratio. Using a the "nursing hours per patient day" is a way to monitor and improve quality of care and service. And in many states, hospitals, clinics, acute care facilities, long-term care and senior living facilities must report the HPPD data to the Department of Public Health. OSHPD's Hospital Disclosure Report measures employment in terms of productive hours for each of RNs, LPNs, unlicensed aides/orderlies, management and supervision, administrative and clerical, and other labor categories. Most hospitals use their payroll system, not their actual unit-level staffing grid, to complete the survey, and thus the data are subject to errors that might exist in any payroll system.

For example, hospitals might not consistently measure hours worked by nurses normally assigned to one unit but "floated" to another. The number of patient days or services provided in each revenue unit is reported, enabling calculation of hours per patient day, hours per patient discharge, and/or hours per service provided. Unit types can be aggregated or examined separately (e.g., HPPD for medical–surgical acute care only). Correlations between the AHA and OSHPD datasets for inpatient days and RN employment were high overall, at least 0.9.

The means of RN and LPN employment were not statistically significantly different, while computed hours per patient day were statistically different in the OSHPD and AHA datasets. Most hospitals provide the staffing data to OSHPD and AHA from payroll systems, which might contain several types of measurement error. First, these systems do not delineate direct patient care from nondirect care in productive staff or hours, and thus overestimate the amount of direct nursing care received by each patient. For example, a nurse might change the unit to which s/he is assigned, without a change in pay, and this change may not be reflected in the payroll data in a timely fashion.

Many researchers and health care leaders want to measure nurse staffing according to the workload of each nurse, although "workload" does not have an agreed-upon definition. Most hospitals can easily report the average number of productive nursing hours per patient day ("hours per patient day" or HPPD), because they keep data on nursing hours and patient days. More importantly, the nurse's patients change as they are admitted and discharged during a shift; thus, a nurse might care for 10 patients during a shift, with the five patients present at the start of the shift being replaced by five other patients later in the shift. Associations between comprehensive nurse staffing characteristics and patient falls and pressure ulcers were examined using negative binomial regression modeling with hospital- and time-fixed effects.

Rates of patient falls and injury falls were found to be greater with higher temporary registered nurse staffing levels but decreased with greater levels of licensed practical nursing care hours per patient day. Some researchers have argued that hours per patient day is the most precise measure of the amount of nursing care provided to patients (Budreau et al. 1999). However, hours per patient day do not accurately measure the impact of admissions, discharges, and transfers on the workload of nurses. Unruh and Fottler have demonstrated that nurse staffing measures that do not adjust for patient turnover underestimate nursing workload and overstate RN staffing levels. While prospective unit-level databases such as CalNOC often include measures of admissions, discharges, and transfers, administrative databases do not include such measures.

The survey requested that hospitals provide data for a representative medical–surgical unit in the hospital. Survey questions focused on nursing hours worked on that unit, discharges and patient days on the unit, nurse-to-patient ratios, number of vacancies, and average time to recruit a RN to the unit. Both hours per patient day and the nurse-to-patient ratio were reported directly by unit managers, enabling a direct comparison of these methods of measuring nurse staffing.

Metrics such as hours per patient day and nursing hours per patient day , referred to by Carter as CHPPD, have been used for decades in the US to examine nursing productivity both within and between hospitals. They are also used to determine staffing levels based on national or regional benchmarks and establish budgets for nursing departments. NHPPD usually refer to qualified nurses in the US, so may include only RNs with degrees or both RNs and those with older diplomas and/or licensed practical nurses. NHPPD are also used in several states in Australia and are usually used to describe qualified nurses only. This is why it is important to understand which staff are included before accepting staffing data. Irvine et al. suggested that it is important to identify and investigate nursing-sensitive quality indicators that are guided by a conceptual framework that can establish relationships between nursing care and patient outcomes.

"Outcomes are affected not only by the care provided, but also by the factors related to the interpersonal aspects of ea re, and to the setting in which care is provided". The process indicator reviewed in this study was Maintenance of Ski n Integrity, referring to the rate per 1,000 patient days at which patients develop pressure ulcers during the course of their hospital stay, but 72 hours or more following admission. For this study, pressure ulcers are defined as a localized area of tissue disturbance that develops when soft tissue is compressed between a bony prominence and external surface for a prolonged period of time. Pressure ulcers are staged from I to IV to classify the degree of tissue damage observed.

Decisions about the adequacy and appropriateness of nurse staffing have long been based on functionally outdated industrial models that focus on work sampling and time-and-motion studies conducted in semi-simulated settings. Even today, the common method of staffing nursing units or identifying the staffing mix in hospitals is by identifying budgeted Nursing Care Hours per Patient Day. A traditional measure of nursing productivity, "nursing hours per patient day has never been satisfactory because 'patient day' as a measure of nursing output takes neither patient acuity nor quality of nursing care into accou nt". . It's been recommended that care hours per patient day be utilized as a staffing and productivity standard. This method addresses nurse-patient ratios versus HPPD, capturing the acuity and actual care hours required to manage specific patient populations by encompassing all care hours and including all support staff. To ensure the optimal use of staffing resources, we must use standardized benchmarks and quality indicators, including patient and staff satisfaction.

We must also consider productive and nonproductive hours , as well as the cost of incremental overtime. 2 The tendency is for nursing managers to fall back on ratio and unit layout and patient location information to make patient assignments. 6 Objective evaluation of patient acuity and needs has shown to improve nurse perception of assignments as more equal and more able to provide safe quality care.

Currently, the Nursing Care Hours per Patient Day formula is the unit of analysis that determines staffing requirements in hospitals. It is a calculable formula that is used as a method of staffing and for budgeting nursing hours . Nursing Care Hours per Patient Day are calculated by multiplying the number of staff delivering direct nursing care by the hours worked during the shift and then dividing that number into the average daily census at a specific designated time. For the purposes of this study, the midnight census was chosen as the designated time. Nursing Care Hours per Patient Day were studied and the data from both Quarters under review were analyzed and compared. Table 2 summarizes the critical care and medical–surgical nurse staffing and patient days data reported by CalNOC and OSHPD.

OSHPD reports a greater number of RN and LPN hours as well as patient days, and all differences are statistically significant. The greater number of nursing hours reported by OSHPD is consistent with OSHPD's productive hours including non-direct-patient-care hours, which may include RNs in special roles such as clinical specialists or infection control managers. The correlations between RN hours, LPN hours, and patient days are relatively high, ranging from 0.73 to 0.92.

Inpatient care unit does not include any hospital-based clinic, long-term care facility, or outpatient hospital department. "Staffing hours per patient day" means the number of full-time equivalent nonmanagerial care staff who will ordinarily be assigned to provide direct patient care divided by the expected average number of patients upon which such assignments are based. "Patient acuity tool" means a system for measuring an individual patient's need for nursing care. Although nursing care hours is commonly used to examine factors related to adverse events among inpatients, the reliability of the NCH measure has rarely been examined. This study assessed the reliability of NCH data from the National Database of Nursing Quality Indicators® by estimating intraclass correlation coefficients with data from the California Office for Statewide Health Planning and Development.

Hospital-level aggregated NCH data for critical care units were linked from each of the databases for 48 California hospitals matched in the two databases. Findings provide evidence that NCH data of the national database were substantially reliable for use in national comparable benchmarking reports for hospitals' quality improvement activities and research. Patient to nurse ratios and nursing hours per patient-day do not capture the true nursing contribution to patient outcomes due to their lack of adjustment for nursing needs. As a result, failure to riskadjust nurse staffing metrics may induce bias into research studies and comparative health care performance measurements, including the misattribution of outcomes to patient factors in place of nursing factors.

Worked Hours Per Patient Day Formula Aims To evaluate and summarise current evidence on the relationship between the patient‐nurse ratio staffing method and nurse employee outcomes. Background Evidence‐based decision‐making linking nurse staffing with staff‐related outcomes is a much needed research area. Although multiple studies have investigated this phenomenon, the evidence is mixed and fragmented. Evaluation A systematic literature search was conducted using Pubmed, Embase, Web of Science, Cinahl, Cochrane Library and the ERIC databases. Key issue Future research should focus on unit‐level data, incorporate other methodologies and aim for comparability between different types of clinical settings as well as different healthcare systems. Conclusion A relationship between the patient‐nurse ratio and specific staff‐related outcomes is confirmed by various studies.

However, apart from the patient‐nurse ratio other variables have to be taken into consideration to ensure quality of care (e.g. skill mix, the work environment and patient acuity). Implications for Nursing Management Hospital management should pursue the access and use of reliable data so that the validity and generalizability of evidence‐based research can be assessed, which in turn can be converted into policy guidelines. The CalNOC data are less widely dispersed than the OSHPD data for the matched set of hospitals, suggesting that the CalNOC data might contain less measurement error.

Nursing hours were on average higher in the OSHPD data than in CalNOC, likely because OSHPD data include nurse staff time spent on activities other than patient care. As a result, the distribution of nurse staffing per patient day is different between these datasets, with the CalNOC data producing somewhat lower hours per patient day than the OSHPD data. The correlations between estimates of hours per patient day are low, at 0.22 for total nursing hours and 0.32 for RN hours.

When possible, hospital units or types of units were matched within each hospital. Productive nursing hours and direct patient care hours were converted to full-time equivalent employment and to nurse-to-patient ratios to compare nurse staffing as measured by different surveys. Aim To describe nurse‐specific and patient risk factors present at the time of a patient fall on medical‐surgical units within an academic public healthcare system. Background The incidence of falls can be devastating for hospitalized patients and their families.

Few studies have investigated how patient and nurse specific factors can decrease the occurrence of falls in hosptials. Method In this retrospective cohort study, data was gathered on all patients who experienced a fall during January 2012‐December 2013. Results Falls were reduced dramatically when the number of nurses on the unit increased to five or six. Patient falls occurred most often when either the least experienced or most experienced nursing staff were providing care.

Conclusion Patient falls in hospitals can be influenced not only by patient specific factors, but by nurse staffing and experience level. Implications for nursing management Findings from this study highlight factors which may contribute to hospital‐based falls prevention initiatives, and are amenable to nursing management decisions. The CWI data illustrate the difficulty of relating hours per patient day to the patient-to-nurse ratio. The correlation between these measures is moderate at best, even when reported by the same person in a single survey. There are several reasons discrepancies might arise between these measurements.

First, standard measures of patient days do not take into account the flow of patients within a day. The patient-to-nurse ratio might better capture fluctuations in patient loads, and might explain why the patient-to-nurse ratio is higher when directly reported than when computed. However, because patient-to-nurse ratios vary during and across shifts they are difficult to measure in a standardized dataset. The patient-to-nurse ratio may be best suited to smaller studies in which shift-to-shift unit-level primary data collection is feasible; its validity and accuracy in hospital-level or aggregated data is questionable. The 3.5 DHPPD staffing requirement, of which 2.4 hours per patient day must be performed by CNAs, is a minimum requirement for SNFs. SNFs shall employ and schedule additional staff and anticipate individual patient needs for the activities of each shift, to ensure patients receive nursing care based on their needs.

The staffing requirement does not ensure that any given patient receives 3.5 or 2.4 DHPPD; it is the total number of actual direct care service hours performed by direct caregivers per patient day divided by the average patient census. There is potential for greater use of unit-level databases collected specifically to analyze nurse staffing and quality of care. Others include the American Nurses Association's National Database of Nursing Quality Indicators , the Department of Veterans Affairs' VA Nursing Outcomes Database , and the Military Nursing Outcomes Database . First, the CalNOC and NDNQI databases depend on voluntary submissions from hospitals, which may affect the representativeness of the data. As noted above, small and for-profit hospitals are under-represented in the CalNOC data.

Hospitals that choose to participate in these data collection efforts may be more interested in quality measurement and improvement, and thus the databases represent better-staffed hospitals. If the data do not represent the full spectrum of staffing patterns, research findings are limited. CalNOC has begun to develop protocols for permitting other researchers to access the data, and CalNOC measures are now being used in the California Hospital Assessment and Reporting Taskforce public reporting project. As data access barriers are addressed, these databases are likely to be more widely used, providing more information for our understanding of nurse staffing patterns and the relationship between nursing and patient outcomes. While we would like to conclude by recommending a single measurement strategy for studies of nurse staffing, such a recommendation is not possible.

Researchers often are limited by data availability, and thus the ideal measures of nurse staffing might not be obtained for every study. Although OSHPD data appear more dispersed than CalNOC data, potentially indicating more measurement error, and the AHA and OSHPD data do not limit their staffing data to direct patient care, the OSHPD and AHA datasets are longitudinal and easily obtained. These datasets can be linked to secondary data on patient outcomes, such as the Nationwide Inpatient Sample and the OSHPD Patient Discharge Data (e.g., Needleman et al. 2001; Mark et al. 2004). Thus, despite their limitations, these datasets should and will continue to be used in research. Researchers should be cognizant of the limitations of these datasets and should consider indices to adjust for the impact of patient turnover on nursing workload.

It is critical that clinical staff have access to HPPD levels and how they are calculated in a particular institution in order to inform their practice. Evidence-based care must use data and research on measures of productivity, patient satisfaction, quality, and financial accountability. In the meantime, some hospitals are implementing a number of strategies to account for the staffing level on all units and to address bottlenecks in processing patient admissions, transfers, and discharges.

At the heart of these activities is a shared desire to provide the best person-centered, timely, efficient, and effective care. The published literature is extensive and describes a variety of uses for tools including establishment setting, daily deployment and retrospective review. There are a variety of approaches including professional judgement, simple volume-based methods (such as patient-to-nurse ratios), patient prototype/classification and timed-task approaches. Tools generally attempt to match staffing to a mean average demand or time requirement despite evidence of skewed demand distributions. The largest group of recent studies reported the evaluation of tools and systems, but provides little evidence of impacts on patient care and none on costs.